SAS^® Grid Computing is a scale-out SAS^® solution that enables SAS applications to better utilize computing resources, which is extremely I/O and compute intensive. It requires the use of a high-performance shared storage (SS) that allows all servers to access the same file systems. SS may be implemented via traditional NFS NAS or clustered file systems (CFS) like GPFS. This paper uses the Lustre* file system, a parallel, distributed CFS, for a case study of performance scalability of SAS Grid Computing nodes on SS. The paper qualifies the performance of a standardized SAS workload running on Lustre at scale. Lustre has been traditionally used for large and sequential I/O. We will record and present the tuning changes necessary for the optimization of Lustre for the SAS applications. In addition, results from the scaling of SAS Cluster jobs running on Lustre will be presented.

Nowadays, most corporations build and maintain their own data warehouse, and an ETL (Extract, Transform, and Load) process plays a critical role in managing the data. Some people might create a large program and execute this program from top to bottom. Others might generate a SAS^® driver with several programs included, and then execute this driver. If some programs can be run in parallel, then developers must write extra code to handle these concurrent processes. If one program fails, then users can either rerun the entire process or comment out the successful programs and resume the job from where the program failed. Usually the programs are deployed in production with read and execute permission only. Users do not have the priviledge of modifying codes on the fly. In this case, how do you comment out the programs if the job terminated abnormally? This paper illustrates an approach for managing ETL process flows. The approach uses a framework based on SAS, on a UNIX platform. This is a high-level infrastructure discussion with some explanation of the SAS codes that are used to implement the framework. The framework supports the rerun or partial run of the entire process without changing any source codes. It also supports the concurrent process, and therefore no extra code is needed.

SID file, SAS^® Deployment Wizard, SAS^® Migration Utility, SAS^® Environment Manager, plan file. SAS^® can seem very mysterious to IT organizations used to working with other software solutions. The more IT knows and understands about SAS how it works, what its system requirements are, how to maintain it and back it up, and what its value is to the organization the better IT can support the SAS shop. This paper provides an introduction to the world of SAS and sheds light on some of the unique elements of maintaining a SAS environment.

The current study looks at recent health trends and behavior analyses of youth in America. Data used in this analysis was provided by the Center for Disease Control and Prevention and gathered using the Youth Risk Behavior Surveillance System (YRBSS). A factor analysis was performed to identify and define latent mental health and risk behavior variables. A series of logistic regression analyses were then performed using the risk behavior and demographic variables as potential contributing factors to each of the mental health variables. Mental health variables included disordered eating and depression/suicidal ideation data, while the risk behavior variables included smoking, consumption of alcohol and drugs, violence, vehicle safety, and sexual behavior data. Implications derived from the results of this research are a primary focus of this study. Risks and benefits of using a factor analysis with logistic regression in social science research will also be discussed in depth. Results included reporting differences between the years of 1991 and 2011. All results are discussed in relation to current youth health trend issues. Data was analyzed using SAS^® 9.3.

One of the first lessons that SAS^® programmers learn on the job is that numeric and character variables do not play well together, and that type mismatches are one of the more common source of errors in their otherwise flawless SAS programs. Luckily, converting variables from one type to another in SAS (that is, casting) is not difficult, requiring only the judicious use of either the input() or put() function. There remains, however, the danger of data being lost in the conversion process. This type of error is most likely to occur in cases of character-to-numeric variable conversion, most especially when the user does not fully understand the data contained in the data set. This paper will review the basics of data storage for character and numeric variables in SAS, the use of formats and informats for conversions, and how to ensure accurate type conversion of even high-precision numeric values.

Complex data manipulations can be resource intensive, both in terms of development time and processing duration. However, in recent years SAS has introduced a number of new technologies that, when used together, can produce a dramatic increase in performance while simultaneously simplifying program development and maintenance. This paper presents a development paradigm that utilizes the problem decomposition capabilities of DS2, the flexibility of SQL, and the performance benefits of in-memory storage using hash objects.

Have you ever wished that with one click you could copy any SAS^® data set, including variable names, so that you could paste the text into a Microsoft Word file, Microsoft PowerPoint slide, or spreadsheet? You can and, with just Base SAS^®, there are some little-known but easy-to use methods that are available for automating many of your (or your users ) common tasks.

Hip fractures are a common source of morbidity and mortality among the elderly. While multiple prior studies have identified risk factors for poor outcomes, few studies have presented a validated method for stratifying patient risk. The purpose of this study was to develop a simple risk score calculator tool predictive of 30-day morbidity after hip fracture. To achieve this, we prospectively queried a database maintained by The American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) to identify all cases of hip fracture between 2005 and 2010, based on primary Current Procedural Terminology (CPT) codes. Patient demographics, comorbidities, laboratory values, and operative characteristics were compared in a univariate analysis, and a multivariate logistic regression analysis was then used to identify independent predictors of 30-day morbidity. Weighted values were assigned to each independent risk factor and were used to create predictive models of 30-day complication risk. The models were internally validated with randomly partitioned 80%/20% cohort groups. We hypothesized that significant predictors of morbidity could be identified and used in a predictive model for a simple risk score calculator. All analyses are performed via SAS^® software.

This paper shows users how they can use a SAS^® macro named %SURVEYGLM to incorporate information about survey design to Generalized Linear Models (GLM). The R function %svyglm (Lumley, 2004) was used to verify the suitability of the %SURVEYGLM macro estimates. The results show that estimates are closer than the R function and that new distributions can be easily added to the algorithm.

Influence analysis in statistical modeling looks for observations that unduly influence the fitted model. Cook s distance is a standard tool for influence analysis in regression. It works by measuring the difference in the fitted parameters as individual observations are deleted. You can apply the same idea to examining influence of groups of observations for example, the multiple observations for subjects in longitudinal or clustered data but you need to adapt it to the fact that different subjects can have different numbers of observations. Such an adaptation is discussed by Zhu, Ibrahim, and Cho (2012), who generalize the subject size factor as the so-called degree of perturbation, and correspondingly generalize Cook s distances as the scaled Cook s distance. This paper presents the %SCDMixed SAS^® macro, which implements these ideas for analyzing influence in mixed models for longitudinal or clustered data. The macro calculates the degree of perturbation and scaled Cook s distance measures of Zhu et al. (2012) and presents the results with useful tabular and graphical summaries. The underlying theory is discussed, as well as some of the programming tricks useful for computing these influence measures efficiently. The macro is demonstrated using both simulated and real data to show how you can interpret its results for analyzing influence in your longitudinal modeling.
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Stepwise regression includes regression models in which the predictive variables are selected by an automated algorithm. The stepwise method involves two approaches: backward elimination and forward selection. Currently, SAS^® has three procedures capable of performing stepwise regression: REG, LOGISTIC, and GLMSELECT. PROC REG handles the linear regression model, but does not support a CLASS statement. PROC LOGISTIC handles binary responses and allows for logit, probit, and complementary log-log link functions. It also supports a CLASS statement. The GLMSELECT procedure performs selections in the framework of general linear models. It allows for a variety of model selection methods, including the LASSO method of Tibshirani (1996) and the related LAR method of Efron et al. (2004). PROC GLMSELECT also supports a CLASS statement. We present a stepwise algorithm for generalized linear mixed models for both marginal and conditional models. We illustrate the algorithm using data from a longitudinal epidemiology study aimed to investigate parents beliefs, behaviors, and feeding practices that associate positively or negatively with indices of sleep quality.

SAS^® functions provide amazing power to your DATA step programming. Some of these functions are essential some of them save you writing volumes of unnecessary code. This paper covers some of the most useful SAS functions. Some of these functions might be new to you, and they will change the way you program and approach common programming tasks.

This paper simply develops a new SAS^® macro, which allows you to scrap user textual reviews from Apple iTunes store for iPhone applications. It not only can help you understand your customers experiences and needs, but also can help you be aware of your competitors user experiences. The macro uses API in iTunes and PROC HTTP in SAS to extract and create data sets. This paper also shows how you can use the application ID and country code to extract user reviews.

The ExcelXP tagset offers several options for controlling column widths, including Width_Points, Width_Fudge, and Absolute_Column_Width. Although Absolute_Column_Width might seem unpredictable at first, it is possible to fix the first two options so that the Absolute_Column_Width is the exact column width in pixels. This poster presents these settings and suggests how to create and manage the integer string of column widths.

Accelerated testing is an effective tool for predicting when systems fail, where the system can be as simple as an engine gasket or as complex as a magnetic resonance imaging (MRI) scanner. In particular, you might conduct an experiment to determine how factors such as temperature and voltage impose enough stress on the system to cause failure. Because system components usually meet nominal quality standards, it can take a long time to obtain failure data under normal-use conditions. An effective strategy is to accelerate the experiment by testing under abnormally stressful conditions, such as higher temperatures. Following that approach, you obtain data more quickly, and you can then analyze the data by using the RELIABILITY procedure in SAS/QC^® software. The analysis is a three-step process: you establish a probability model, explore the relationship between stress and failure, and then extrapolate to normal-use conditions. Graphs are a key component of all three stages: you choose a model by comparing residual plots from candidate models, use graphs to examine the stress-failure relationship, and then use an appropriately scaled graph to extrapolate along a straight line. This paper guides you through the process, and it highlights features added to the RELIABILITY procedure in SAS/QC 13.1.

Structured Query Language (SQL) does not recognize the concept of row order. Instead, query results are thought of as unordered sets of rows. Most workarounds involve including serial numbers, which can then be compared or subtracted. This presentation illustrates and compares five techniques for creating serial numbers.

The SAS^® Data Quality Server allows SAS^® programmers to integrate the power of DataFlux^® into their data cleaning programs. The power of SAS Data Quality Server enables programmers to efficiently identify matching records across different datasets when exact matches are not present. During a recent educational research project, the DQMATCH function proved very capable when trying to link records from disparate data sources. Two key insights led to even greater success in linking records. The first insight was acknowledging that the hierarchical structure of data can greatly improve success in matching records. The second insight was that the names of individuals can be restructured to improve the chances of successful matches. This paper provides an overview of how these insights were implemented using the DQMATCH function to link educational data from multiple sources.

Cluster (group) randomization in trials is increasingly used over patient-level randomization. There are many reasons for this, including more pragmatic trials associated with comparative effectiveness research. Examples of clusters that could be randomized for study are clinics or hospitals, counties within a state, and other geographical areas such as communities. In many of these trials, the number of clusters is relatively small. This can be a problem if there are important covariates at the cluster level that are not balanced across the intervention and control groups. For example, if we randomize eight counties, a simple randomization could put all counties with high socioeconomic status in one group or the other, leaving us without good comparison data. There are strategies to prevent an unlucky cluster randomization. These include matching, stratification, minimization and covariate-constrained randomization. Each method is discussed, and a county-level Health Economics example of covariate-constrained randomization is shown for intermediate SAS^® users working with SAS^® Foundation for Release 9.2 and SAS/STAT^® on a Windows operating system.

The Base SAS^® 9.4 Output Delivery System (ODS) EPUB destination enables users to deliver SAS^® reports as e-books on Apple mobile devices. The first maintenance release of SAS^® 9.4 adds the ODS EPUB3 destination, which offers powerful new multimedia and presentation features to report writers. This paper shows you how to include images, audio, and video in your ODS EPUB3 e-book reports. You learn how to use publishing presentation techniques such as sidebars and multicolumn layouts. You become familiar with best practices for accessibility when employing these new features in your reports. This paper provides advanced instruction for writing e-books with ODS EPUB. Please bring your iPad, iPhone, or iPod to the presentation so that you can download and read the examples.

SAS^® 9.4 has overhauled web authentication schemes, and the integration with enterprise security infrastructure is quite different from that of SAS^® 9.3. This paper examines advanced security features such as Secure Sockets Layer (SSL) configuration, single sign-on (SSO) support through Integrated Windows authentication (IWA), and third-party security packages like CA SiteMinder and IBM Tivoli Access Manager and WebSEAL. FIPS 140-2 compliance efforts that enforce the use of a stronger encryption algorithm for web communication and the SAS^® system itself are also described. The authentication support for mobile devices such as the iPad is different. The secure Wi-Fi connection from a mobile device to the IT internal resources, as well as how it can be safely integrated into the enterprise security configuration by using the same user repository as the SAS web applications, is explained. The configuration example is shown with SAS^® Visual Analytics 6.2.

The operational tempo of marketing in a digital world seems faster every day. New trends, issues, and ideas appear and spread like wildfire, demanding that sales and marketing adapt plans and priorities on-the-fly. The technology available to us can handle this, but traditional organizational processes often become the bottleneck. The solution is a new management approach called agile marketing. Drawing upon the success of agile software development and the lean start-up movement, agile marketing is a simple but powerful way to make marketing teams more nimble and marketing programs more responsive. You don't have to be small to be agile agile marketing has thrived at large enterprises such as Cisco and EMC. This session covers the basics of agile marketing what it is, why it works, and how to get started with agile marketing in your own team. In particular, we look at how agile marketing dovetails with the explosion of data-driven management in marketing by using the fast feedback from analytics to rapidly iterate and adapt new marketing programs in a rapid yet focused fashion.

Allocation is key. If the allocation isn t right, it can lead to out-of-stocks, lost sales, and customer dissatisfaction. Automating your most complicated and time-consuming tasks, and ensuring you are feeding the right data at the right time, is critical. This session will review how Beall s Outlet and Beall s Department Stores are managing and executing allocations at optimal levels. How using attributes and group definitions allow them to be responsive to trends, history, and plans.

SAS^® 9.4 and SAS^® Visual Analytics support a wide list of authentication protocols such as Integrated Windows authentication (IWA), client certificate, IBM WebSEAL, CA SiteMinder, and Security Assertion Markup Language (SAML) 2.0. However, advanced customers might want to use some of these protocols together and also have the flexibility to select which protocols to use. In this paper, we focus on a fallback authentication framework that supports IWA as the primary authentication method. When IWA fails, it uses the X509 client certificate as the secondary authentication method, and when the client certificate fails, it uses the form-based username/password as the last option. The paper first introduces the security architecture of SAS^® 9.4 and SAS Visual Analytics. It then reviews the three above-mentioned security protocols. Further, it introduces the detailed fallback authentication framework and discusses how to configure it. Finally, we discuss the use of this framework in the customer scenario from implementing the fallback authentication framework in a customer s SAS^® 9.4 and SAS Visual Analytics environment.

The videogame industry is a growing business, with an annual growth rate that exceeded 16.7% for the period 2005 through 2008. Moreover, revenues from online games will account for more than 38% of total video game software revenues by 2013. Due to this, online games are vulnerable to illicit player activity that results in cheating. Cheating in online games could damage the reputation of the game when honest players realize that their peers are cheating, resulting in the loss of trust from honest players, and ultimately reducing revenue for the gameproducers. Analysis of game data is fundamental for understanding player behaviors and for combating cheating in online games. In this presentation, we propose a data analysis methodology for detecting cheating in massive multiplayer online (MMO) racing games. More specifically, our work focuses on bot detection. A bot controls a player automatically and is characterized by repetitive behavior. Players in an MMO racing game can use bots to play during the races using artificial intelligence favoring their odds to win, and can automate the process of starting a new race upon finishing the last one. This results in a high number of races played with race duration showing low mean and low standard deviation, and time in between races showing consistent low median value. A study case is built on upon data from an MMO racing game. Our results indicate that our methodology successfully characterize suspiciousplayer behavior.

Finding groups with similar attributes is at the core of knowledge discovery. To this end, Cluster Analysis automatically locates groups of similar observations. Despite successful applications, many practitioners are uncomfortable with the degree of automation in Cluster Analysis, which causes intuitive knowledge to be ignored. This is more true in text mining applications since individual words have meaning beyond the data set. Discovering groups with similar text is extremely insightful. However, blind applications of clustering algorithms ignore intuition and hence are unable to group similar text categories. The challenge is to integrate the power of clustering algorithms with the knowledge of experts. We demonstrate how SAS/STAT^® 9.2 procedures and the SAS^® Macro Language are used to ensemble the opinion of domain experts with multiple clustering models to arrive at a consensus. The method has been successfully applied to a large data set with structured attributes and unstructured opinions. The result is the ability to discover observations with similar attributes and opinions by capturing the wisdom of the crowds whether man or model.

SAS/ACCESS^® Interface to ODBC has been around forever. On one level, ODBC is very easy to use. That ease hides the flexibility that ODBC offers. This presentation uses examples to show you how to increase your program's performance and troubleshoot problems. You will learn: the differences between ODBC and OLE DB what the odbc.ini file is (and why it is important) how to discover what your ODBC driver is actually doing the difference between a native ACCESS engine and SAS/ACCESS Interface to ODBC

This paper expands upon A Multilevel Model Primer Using SAS^® PROC MIXED in which we presented an overview of estimating two- and three-level linear models via PROC MIXED. However, in our earlier paper, we, for the most part, relied on simple options available in PROC MIXED. In this paper, we present a more advanced look at common PROC MIXED options used in the analysis of social and behavioral science data, as well introduce users to two different SAS macros previously developed for use with PROC MIXED: one to examine model fit (MIXED_FIT) and the other to examine distributional assumptions (MIXED_DX). Specific statistical options presented in the current paper include (a) PROC MIXED statement options for estimating statistical significance of variance estimates (COVTEST, including problems with using this option) and estimation methods (METHOD =), (b) MODEL statement option for degrees of freedom estimation (DDFM =), and (c) RANDOM statement option for specifying the variance/covariance structure to be used (TYPE =). Given the importance of examining model fit, we also present methods for estimating changes in model fit through an illustration of the SAS macro MIXED_FIT. Likewise, the SAS macro MIXED_DX is introduced to remind users to examine distributional assumptions associated with two-level linear models. To maintain continuity with the 2013 introductory PROC MIXED paper, thus providing users with a set of comprehensive guides for estimating multilevel models using PROC MIXED, we use the same real-world data sources that we used in our earlier primer paper.

The use of Bayesian methods has become increasingly popular in modern statistical analysis, with applications in numerous scientific fields. In recent releases, SAS^® has provided a wealth of tools for Bayesian analysis, with convenient access through several popular procedures in addition to the MCMC procedure, which is specifically designed for complex Bayesian modeling (not discussed here). This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then describes the Bayesian capabilities provided in four procedures(the GENMOD, PHREG, FMM, and LIFEREG procedures) including the available prior distributions, posterior summary statistics, and convergence diagnostics. Various sampling methods that are used to sample from the posterior distributions are also discussed. The second part of the paper describes how to use the GENMOD and PHREG procedures to perform Bayesian analyses for real-world examples and how to take advantage of the Bayesian framework to address scientific questions.

This paper is an introduction to SAS^® Studio and covers how to perform basic programming tasks in SAS Studio. Many people program in the SAS^® language by using SAS Display Manager or SAS^® Enterprise Guide^®. SAS Studio is different because it enables you to write and run SAS code by using the most popular web browsers, without requiring a SAS^® 9.4 installation on your machine. With SAS Studio, you can access your data files, libraries, and existing programs, and write new programs while using SAS software behind the scenes. SAS Studio connects to a SAS sever in order to process SAS programs. The SAS server can be a hosted server in a cloud environment, a server in your local environment, or a copy of SAS on your local machine.

The Kolmogorov-Smirnov (K-S) test is one of the most useful and general nonparametric methods for comparing two samples. It is sensitive to all types of differences between two populations (shift, scale, shape, and so on). In this paper, we will present a thorough investigation into the K-S test including, derivation of the formal test procedure, practical demonstration of the test, large sample approximation of the test, and ease of use in SAS^® using the NPAR1WAY procedure.

SAS^® and SAS^® Enterprise Miner^™ have provided advanced data mining and machine learning capabilities for years beginning long before the current buzz. Moreover, SAS has continually incorporated advances in machine learning research into its classification, prediction, and segmentation procedures. SAS Enterprise Miner now includes many proven machine learning algorithms in its high-performance environment and is introducing new leading-edge scalable technologies. This paper provides an overview of machine learning and presents several supervised and unsupervised machine learning examples that use SAS Enterprise Miner. So, come back to the future to see machine learning in action with SAS!
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Big data! Hadoop! MapReduce! These are all buzzwords that you ve probably already heard mentioned at SAS^® Global Forum 2014. But what exactly is MapReduce and what has it got to do with SAS^®? This talk explains how a simple processing framework (created by Google and more recently popularized by the open-source technology Hadoop) can be replicated using cornerstone SAS technologies such as Base SAS^®, SAS macros, and SAS/CONNECT^®. The talk explains how, out of the box, the SAS DATA step can replicate the MAP function. It looks at how well-established SAS procedures can be used to create reduce-like functionality. We look at how parallel processing data across multiple machines using MPCONNECT can replicate MapReduce s shared-nothing approach to data processing.

Overdispersion (extra variation) arises in binomial, multinomial, or count data when variances are larger than those allowed by the binomial, multinomial, or Poisson model. This phenomenon is caused by clustering of the data, lack of independence, or both. As pointed out by McCullagh and Nelder (1989), Overdispersion is not uncommon in practice. In fact, some would maintain that over-dispersion is the norm in practice and nominal dispersion the exception. Several approaches are found for handling overdispersed data, namely quasi-likelihood and likelihood models, generalized estimating equations, and generalized linear mixed models. Some classical likelihood models are presented. Among them are the beta-binomial, binomial cluster (a.k.a. random clumped binomial), negative-binomial, zero-inflated Poisson, zero-inflated negative-binomial, hurdle Poisson, and the hurdle negative-binomial. We focus on how these approaches or models can be implemented in a practical way using, when appropriate, the procedures GLIMMIX, GENMOD, FMM, COUNTREG, NLMIXED, and SURVEYLOGISTIC. Some real data set examples are discussed in order to illustrate these applications. We also provide some guidance on how to analyze generalized linear overdispersion mixed models and possible scenarios where we might encounter them.

The proliferation of textual data in business is overwhelming. Unstructured textual data is being constantly generated via call center logs, emails, documents on the web, blogs, tweets, customer comments, customer reviews, and so on. While the amount of textual data is increasing rapidly, businesses ability to summarize, understand, and make sense of such data for making better business decisions remain challenging. This presentation takes a quick look at how to organize and analyze textual data for extracting insightful customer intelligence from a large collection of documents and for using such information to improve business operations and performance. Multiple business applications of case studies using real data that demonstrate applications of text analytics and sentiment mining using SAS^® Text Miner and SAS^® Sentiment Analysis Studio are presented. While SAS^® products are used as tools for demonstration only, the topics and theories covered are generic (not tool specific).

In randomized experiments, it is generally assumed that the hierarchical structures and variances are the same in the treatment and control groups. In some situations, however, these structures and variance components can differ. Consider a randomized experiment in which individuals randomized to the treatment condition are further assigned to clusters in which the intervention is administered, but no such clustering occurs in the control condition. Such a structure can occur, for example, when the individuals in the treatment condition are randomly assigned to group therapy sessions or to mathematics tutoring groups; individuals in the control condition do not receive group therapy or mathematics tutoring and therefore do not have that level of clustering. In this example, individuals in the treatment condition have a hierarchical structure, but individuals in the control condition do not. If the therapists or tutors differ in efficacy, the clustering in the treatment condition induces an extra source of variability in the data that needs to be accounted for in the analysis. We show how special features of SAS^® PROC MIXED and PROC GLIMMIX can be used to analyze data in which one or more treatment groups have a hierarchical structure that differs from that in the control group. We also discuss how to code variables in order to increase the computational efficiency for estimating parameters from these designs.

SAS/STAT^® 13.1 includes the new ICLIFETEST procedure, which is specifically designed for analyzing interval-censored data. This type of data is frequently found in studies where the event time of interest is known to have occurred not at a specific time but only within a certain time period. PROC ICLIFETEST performs nonparametric survival analysis of interval-censored data and is a counterpart to PROC LIFETEST, which handles right-censored data. With similar syntax, you use PROC ICLIFETEST to estimate the survival function and to compare the survival functions of different populations. This paper introduces you to the ICLIFETEST procedure and presents examples that illustrate how you can use it to perform analyses of interval-censored data.

Hierarchical data are common in many fields, from pharmaceuticals to agriculture to sociology. As data sizes and sources grow, information is likely to be observed on nested units at multiple levels, calling for the multilevel modeling approach. This paper describes how to use the GLIMMIX procedure in SAS/STAT^® to analyze hierarchical data that have a wide variety of distributions. Examples are included to illustrate the flexibility that PROC GLIMMIX offers for modeling within-unit correlation, disentangling explanatory variables at different levels, and handling unbalanced data. Also discussed are enhanced weighting options, new in SAS/STAT 13.1, for both the MODEL and RANDOM statements. These weighting options enable PROC GLIMMIX to handle weights at different levels. PROC GLIMMIX uses a pseudolikelihood approach to estimate parameters, and it computes robust standard error estimators. This new feature is applied to an example of complex survey data that are collected from multistage sampling and have unequal sampling probabilities.

A central component of discussions of healthcare reform in the U.S. are estimations of healthcare cost and use at the national or state level, as well as for subpopulation analyses for individuals with certain demographic properties or medical conditions. For example, a striking but persistent observation is that just 1% of the U.S. population accounts for more than 20% of total healthcare costs, and 5% account for almost 50% of total costs. In addition to descriptions of specific data sources underlying this type of observation, we demonstrate how to use SAS^® to generate these estimates and to extend the analysis in various ways; that is, to investigate costs for specific subpopulations. The goal is to provide SAS programmers and healthcare analysts with sufficient data-source background and analytic resources to independently conduct analyses on a wide variety of topics in healthcare research. For selected examples, such as the estimates above, we concretely show how to download the data from federal web sites, replicate published estimates, and extend the analysis. An added plus is that most of the data sources we describe are available as free downloads.

A SAS^® license of any organization consists of a variety of SAS components such as SAS/STAT^®, SAS/GRAPH^®, SAS/OR^®, and so on. SAS administrators do not have any automated tool supplied with Base SAS^® software to find how many licensed copies are being actively used, how many SAS users are actively utilizing the SAS server, and how many SAS datasets are being referenced. These questions help a SAS administrator to take important decisions such as controlling SAS licenses, removing inactive SAS users, purging long-time non-referenced SAS data sets, and so on. With the help of a system parameter that is provided by SAS and called RTRACE, these questions can be answered. The goal of this paper is to explain the setup of the RTRACE parameter and to explain its use in making the SAS administrator s life easy. This paper is based on SAS^® 9.2 running on AIX operating system.

Sampling is widely used in different fields for quality control, population monitoring, and modeling. However, the purposes of sampling might be justified by the business scenario, such as legal or compliance needs. This paper uses one probability sampling method, stratified sampling, combined with quality control review business cost to determine an optimized procedure of sampling that satisfies both statistical selection criteria and business needs. The first step is to determine the total number of strata by grouping the strata with a small number of sample units, using box-and-whisker plots outliers as a whole. Then, the cost to review the sample in each stratum is justified by a corresponding business counter-party, which is the human working hour. Lastly, using the determined number of strata and sample review cost, optimal allocation of predetermined total sample population is applied to allocate the sample into different strata.

The power of social media has increased to such an extent that businesses that fail to monitor consumer responses on social networking sites are now clearly at a disadvantage. In this paper, we aim to provide some insights on the impact of the Digital Rights Management (DRM) policies of Microsoft and the release of Xbox One on their customers' reactions. We have conducted preliminary research to compare the basic text mining capabilities of SAS^® and R, two very diverse yet powerful tools. A total of 6,500 Tweets were collected to analyze the impact of the DRM policies of Microsoft. The Tweets were segmented into three groups based on date: before Microsoft announced its Xbox One policies (May 18 to May 26), after the policies were announced (May 27 to June 16), and after changes were made to the announced policies (June 16 to July 1). Our results suggest that SAS works better than R when it comes to extensive analysis of textual data. In our following work, customers reactions to the release of Xbox One will be analyzed using SAS^® Sentiment Analysis Studio. We will collect Tweets on Xbox posted before and after the release of Xbox One by Microsoft. We will have two categories, Tweets posted between November 15 and November 21 and those posted between November 22 and November 29. Sentiment analysis will then be performed on these Tweets, and the results will be compared between the two categories.

This presentation is an open-ended discussion about techniques for transferring data and analytical results from SAS^® to Microsoft Excel. There will be some introductory comments, but this presentation does not have any set content. Instead, the topics discussed are dictated by attendee questions. Come prepared to ask and get answers to your questions. To submit your questions or suggestions for discussion in advance, go to http://support.sas.com/surveys/askvince.html.

Most marketers today are trying to use Facebook s network of 1.1 billion plus registered users for social media marketing. Local television stations and newspapers are no exception. This paper investigates what makes a post effective. A Facebook page that is owned by a brand has fans, or people who like the page and follow the stories posted on that page. The posts on a brand page, however, do not appear on all the fans News Feeds. This is determined by EdgeRank, a Facebook proprietary algorithm that determines what content users see and how it s prioritized on their News Feed. If marketers can understand how EdgeRank works, then they can develop more impactful posts and ultimately produce more effective social marketing using Facebook. The objective of this paper is to find the characteristics of a Facebook post that enhance the efficacy of a news outlet s page among their fans using Facebook Power Ratio as the target variable. Power Ratio, a surrogate to EdgeRank, was developed by experts at Frank N. Magid Associates, a research-based media consulting firm. Seventeen variables that describe the characteristics of a post were extracted from more than 8,000 posts, which were encoded by 10 media experts at Magid. Numerous models were built and compared to predict Power Ratio. The most useful model is a polynomial regression with the top three important factors as whether a post asks fans to like the post, content category of a post (including news, weather, etc.), and number of fans of the page.

The Challenge: assigning outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads, and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones in order to maximize productivity and availability. Our Solution: use the SAS/OR^® procedure PROC CLP to formulate the challenge as a constraint satisfaction problem (CSP) since the objective is not necessarily to minimize a cost function, but rather to find a feasible solution to the constraint set. The input consists of the number of agents, the number of districts, the number of leads in each district, the desired number of leads per agent, the amount by which the actual number of leads can differ from the desired number, and the time zone for each district.

Many different neuroscience researchers have explored how various parts of the brain are connected, but no one has performed association mining using brain data. In this study, we used SAS^® Enterprise Miner^™ 7.1 for association mining of brain data collected by a 14-channel EEG device. An application of the association mining technique is presented in this novel context of brain activities and by linking our results to theories of cognitive neuroscience. The brain waves were collected while a user processed information about Facebook, the most well-known social networking site. The data was cleaned using Independent Component Analysis via an open source MATLAB package. Next, by applying the LORETA algorithm, activations at every fraction of the second were recorded. The data was codified into transactions to perform association mining. Results showing how various parts of brain get excited while processing the information are reported. This study provides preliminary insights into how brain wave data can be analyzed by widely available data mining techniques to enhance researcher s understanding of brain activation patterns.

Suicidal tendency among adolescent girls is a big challenge for the present day society. The main goal of this paper is to find associations of the various socio-emotional factors to suicidal tendencies among adolescent girls in the United States. The data were obtained from the National Longitudinal Study of Adolescent Health to explore social behavior among adolescents. The observations from National Longitudinal Study of Adolescent Health represent a nationally representative sample of adolescents in grades 7 through 12 in the U.S. Students in each school were stratified by grade and sex. About 17 students were randomly chosen from each stratum so that a total of approximately 200 adolescents were selected from each of the 80 pairs of schools. The public access sample includes 6,504 adolescents. Models built via multiple regressions are used to find the association between depression, religious affiliation, and suicidal tendency. ANOVA and chi-square tests are conducted to confirm the association of religious affiliation, depression to suicidal tendency.

With a growing enterprise analytics environment that comprises global users and a variety of sensitive data sources, a system administrator is faced with the challenge of knowing who logs into the system, how often, and what applications and what data sources are being consumed. This information is necessary for auditing the consumers of data as well as for monitoring the growth of data sources for hardware expansion. With the use of SAS^® Audit, Performance and Measurement Package, along with some additional middle-tier logging and SAS^® code, information about the major consumers of the environment can be loaded into LASR tables and analyzed with SAS^® Visual Analytics reporting tools.

Do you have an abstract for an idea that you want to submit as a proposal to SAS^® conferences, but you are not sure which section is the most appropriate one? In this paper, we discuss a methodology for automatically identifying the most suitable section or sections for your proposal content. We use SAS^® Text Miner 12.1 and SAS^® Content Categorization Studio 12.1 to develop a rule-based categorization model. This model is used to automatically score your paper abstract to identify the most relevant and appropriate conference sections to submit to for a better chance of acceptance.

In our previous work, we often needed to perform large numbers of repetitive and data-driven post-campaign analyses to evaluate the performance of marketing campaigns in terms of customer response. These routine tasks were usually carried out manually by using Microsoft Excel, which was tedious, time-consuming, and error-prone. In order to improve the work efficiency and analysis accuracy, we managed to automate the analysis process with SAS^® programming and replace the manual Excel work. Through the use of SAS macro programs and other advanced skills, we successfully automated the complicated data-driven analyses with high efficiency and accuracy. This paper presents and illustrates the creative analytical ideas and programming skills for developing the automatic analysis process, which can be extended to apply in a variety of business intelligence and analytics fields.

This is a simple macro that will examine all fields in a SAS^® dataset that are stored as character data to see if they contain real character data or if they could be converted to numeric. It then performs the conversion and reports in the log the names of any fields that could not be converted. This allows the truly numeric data to be analyzed by PROC MEANS or PROC UNIVARIATE. It makes use of the SAS dictionary tables, the SELECT INTO syntax, and the ANYALPHA function.

The creation of production reports for our organization has historically been a labor-intensive process. Each month, our team produced around 650 SAS^® graphs and 30 tables which were then copied and pasted into 16 custom Microsoft PowerPoint presentations, each between 20 and 30 pages. To reduce the number of manual steps, we converted to using stored processes and the SAS^® Add-In for Microsoft Office. This allowed us to simply refresh those 16 PowerPoint presentations by using SAS Add-In for Microsoft Office to run SAS^® Stored Processes. SAS Stored Processes generates the graphs and tables while SAS Add-In for Microsoft Office refreshes the document with updated graphs already sized and positioned on the slides just as we need them. With this new process, we are realizing the dream of reducing the amount of time spent on a single monthly production process. This paper will discuss the steps to creating a complex PowerPoint presentation that is simply refreshed rather than created new each month. I will discuss converting the original code to stored processes using SAS^® Enterprise Guide^®, options and style statements that are required to continue to use a custom style sheet, and how to create the PowerPoint presentation with an assortment of output types including horizontal bar charts, control charts, and tables. I will also discuss some of the challenges and solutions specific to the stored process and PowerPoint Add-In that we encountered during this conversion process.

This paper kicks off a project to write a comprehensive book of best practices for documenting SAS^® projects. The presenter s existing documentation styles are explained. The presenter wants to discuss and gather current best practices used by the SAS user community. The presenter shows documentation styles at three different levels of scope. The first is a style used for project documentation, the second a style for program documentation, and the third a style for variable documentation. This third style enables researchers to repeat the modeling in SAS research, in an alternative language, or conceptually.

As IT professionals, saving time is critical. Delivering timely and quality-looking reports and information to management, end users, and customers is essential. SAS^® provides numerous 'canned' PROCedures for generating quick results to take care of these needs ... and more. In this hands-on workshop, attendees acquire basic insights into the power and flexibility offered by SAS PROCedures using PRINT, FORMS, and SQL to produce detail output; FREQ, MEANS, and UNIVARIATE to summarize and create tabular and statistical output; and data sets to manage data libraries. Additional topics include techniques for informing SAS which data set to use as input to a procedure, how to subset data using a WHERE statement (or WHERE= data set option), and how to perform BY-group processing.

There is an ever-increasing number of study designs and analysis of clinical trials using Bayesian frameworks to interpret treatment effects. Many research scientists prefer to understand the power and probability of taking a new drug forward across the whole range of possible true treatment effects, rather than focusing on one particular value to power the study. Examples are used in this paper to show how to compute Bayesian probabilities using the SAS/STAT^® MIXED procedure and UNIVARIATE procedure. Particular emphasis is given to the application on efficacy analysis, including the comparison of new drugs to placebos and to standard drugs on the market.

As complicated as the macro language is to learn, there are very strong reasons for doing so. At its heart, the macro language is a code generator. In its simplest uses, it can substitute simple bits of code like variable names and the names of data sets that are to be analyzed. In more complex situations, it can be used to create entire statements and steps based on information may even be unavailable to the person writing or even executing the macro. At the time of execution, it can be used to make queries of the SAS^® environment as well as the operating system, and utilize the gathered information to make informed decisions about how it is to further function and execute.

Because the macro language is primarily a code generator, it makes sense that the code that it creates must be generated before it can be executed. This implies that execution of the macro language comes first. Simple as this is in concept, timing issues and conflicts are often not so simple to recognize in application. As we use the macro language to take on more complex tasks, it becomes even more critical that we have an understanding of these issues.

Macro variables and their values are stored in symbol tables, which in turn are held in memory. Not only are there are a number of ways to create macro variables, but they can be created in a wide variety of situations. How they are created and under what circumstances effects the variable s scope how and where the macro variable is stored and retrieved. There are a number of misconceptions about macro variable scope and about how the macro variables are assigned to symbol tables. These misconceptions can cause problems that the new, and sometimes even the experienced, macro programmer does not anticipate. Understanding the basic rules for macro variable assignment can help the macro programmer solve some of these problems that are otherwise quite mystifying.

The number of SAS deployments on Red Hat Enterprise Linux (RHEL) continues to increase in recent years because more and more customers have found RHEL to be the best price/performance choice for new and/or updated SAS deployments on x86 systems. Back for the fourth year at SGF, Barry will share new performance findings and best practices for deploying SAS on Red Hat Enterprise Linux and will discuss topics such as virtualization, GFS2 shared file system, SAS Grid Manager, and more. This session will be beneficial for SAS customers interested in deploying on Red Hat Enterprise Linux, or existing SAS-on-RHEL customers who want to get more out of their deployments.

There are many components that make up the middle tier and server tier of a SAS^® 9.4 deployment. There is also a variety of technologies that can be used to provide high availability of these components. This paper focuses on a small set of best practices recommended by SAS for a consistent high-availability strategy across the entire SAS 9.4 platform. We focus on two technologies: clustering, as well as the high-availability features of SAS^® Grid Manager. For the clustering, we detail newly introduced clustering capabilities in SAS 9.4 such as the middle-tier SAS^® Web Application Server and the server-tier SAS^® metadata clusters. We also introduce the small, medium, and large deployment scenarios or profiles, which make use of each of these technologies. These deployment scenarios reflect the typical customer's environment and address their high availability, performance, and scalability requirements.

This paper provides an overview of how to create a SAS^® Enterprise Guide^® process that is well designed, simple, documented, automated, modular, efficient, reliable, and easy to maintain. Topics include how to organize a SAS Enterprise Guide process, how to best document in SAS Enterprise Guide, when to leverage point-and-click functionality, and how to automate and simplify SAS Enterprise Guide processes. This paper has something for any SAS Enterprise Guide user, new or experienced!

You can provide access and visibility to SAS^® BI Dashboards, SAS^® Stored Processes, and SAS^® Visual Analytics through the use of SAS^® Web Parts for Microsoft SharePoint. In many organizations, the administrators who are responsible for SharePoint and SAS^® are different. This paper provides best practices for the deployment of SAS Web Parts for Microsoft SharePoint. Bridging the gap between SharePoint and SAS is especially important for people who are not familiar with SharePoint administration. This paper also provides tips for co-existence between SAS Web Parts for Microsoft SharePoint 6.1 and 5.1. (The 5.1 release is available in SAS^® 9.3. The 6.1 release is available in SAS^® 9.4.) Finally, this paper provides some guidance on DNS, permissions, and installation techniques the fine points that make or break your deployment!

The scheduling of surgical operations in a hospital is a complex problem, with each surgical specialty having to satisfy their demand while competing for resources with other hospital departments. This project extends the construction of a weekly timetable, the Master Surgery Schedule, which assigns surgical specialties to operating theater sessions by taking into account the post-surgery resource requirements, primarily post-operative beds on hospital wards. Using real data from the largest teaching hospital in Wales, UK, this paper describes how SAS^® has been used to analyze large data sets to investigate the relationship between the operating theater schedule and the demand for beds on wards in the hospital. By understanding this relationship, a more well-informed and robust operating theater schedule can be produced that delivers economic benefit to the hospital and a better experience for the patients by reducing the number of cancelled operations caused by the unavailability of beds on hospital wards.

SAS^® Visual Analytics and the SAS^® LASR™ Analytic Server provide many capabilities to analyze data fast. Depending on your organization, data can be loaded as a self-service operation. Or, your data can be large and shared with many people. And, as data gets large, effectively loading it and keeping it updated become important. This presentation discusses the range of data scenarios from self-service spreadsheets to very large databases, from single-subject data to large star schema topologies, and from single-use data to continually updated data that requires high levels of resilience and monitoring. Fast and easy access to big data is important to empower your organization to make better business decisions. Understanding how to have a responsive and reliable data tier on which to make these decisions is within your reach.

Rapid adoption of high-performance scalable systems supporting big data acquisition, absorption, management, and analysis has exposed a potential gap in asserting governance and oversight over the information production flow. In a traditional organizational data management environment, business rules encompassed within data controls can be used to govern the creation and consumption of data across the enterprise. However, as more big data analytics applications absorbing massive data sets from external sources whose creation points are far removed from their various repurposed uses, the ability to control the production of data must give way to a different kind of data governance. In this talk, we discuss a rational approach to scoping data governance for big data. Instead of presuming the ability to validate and cleanse data prior to its loading into the analytical platform, we will explore pragmatic expectations and measures for scoring data utility and believability within the context of big data analytics applications. Attendees will learn about: Expectations for data variation The impact of variance on analytical results Focusing on the quality of your data management processesand infrastructure Measurements for data usability

IBM is unique in having developed enterprise class big data software and systems that allow you to address the full spectrum of big data business challenges. The centerprice of this strategy is IBM InfoSphere BigInsights, which brings the power of Hadoop to the enterprise and reliably manages large volumes of structured and unstructured data. BigInsights makes it simpler for people to use Hadoop and build big data applications. It enhances this open source technology to withstand the demands of your enterprise, adding administrative, discovery, development, provisioning, and security features. Attend this session and find out how IBM platforms and SAS software can deliver these capabilities for you.

The emerging discipline of data governance encompasses data quality assurance, data access and use policy, security risks and privacy protection, and longitudinal management of an organization s data infrastructure. In the interests of forestalling another bureaucratic solution to data governance issues, this presentation features database programming tools that provide rapid access to big data and make selective access to and restructuring of metadata practical.

In many organizations the amount of data we deal with increases far faster than the hardware and IT infrastructure to support them. As a result, we encounter significant bottlenecks and I/O bound processes. However, clever use of SAS^® software can help us find a way around. In this paper we will look at the clever use of PROC OLAP to show you how to address I/O bound processing spread I/O traffic to different servers to increase cube building efficiency. This paper assumes experience with SAS^® OLAP Cube Studio and/or PROC OLAP.

Digital data has manifested into a classic BIG DATA challenge for marketers who want to push past the retroactive analysis limitations of traditional web analytics. The current groundswell of digital device adoption and variety of digital interactions grows larger year after year. The opportunity for 'digital intelligence' has arrived, as traditional web analytic techniques were not designed for the breadth of channels, devices, and pace that fuels consumer experiences. In parallel, today's landscape for data visualization, advanced analytics, and our ability to process very large amounts of multi-channel information is changing. The democratization of analytics for the masses is upon us, and marketers have the oppourtunity to take advantage of descriptive, predictive, and (most importantly) prescriptive data-driven insights. This presentation describes how organizations can use SAS^® products, specifically SAS^® Visual Analytics and SAS^® Adaptive Customer Experience, to overcome the limitations of web analytics, and support data-driven integrated marketing objectives.

Simply using an ODS destination to replay PROC CONTENTS output does not provide the user with attractive, usable metadata. Harness the power of SAS^® and ODS output objects to create designer multi-tab metadata workbooks with the click of a mouse!

In the credit card industry, there is a group of people who use credit cards as an interest-free loan by transferring their balances between cards during 0% balance transfer (BT) periods in order to avoid paying interest. These people are called gamers. Gamers generate losses for banks due to their behavior of paying no interest and having no purchases. It is hard to use traditional ways, such as risk scorecards, to identify them since gamers tend to have very good credit histories. This paper uses Naive Bayes classifier to classify gamers into three segments, according to the proportion of gamers. Using this model, the targeting policy and underwriting policy can be significantly improved and the function of tracking the proportion of gamers in population can be realized. This result has been accomplished by using logistic regression in SAS^® combined with a Microsoft Excel pivot table. The procedure is described in detail in this paper.

Discover how SAS^® leverages field marketing programs to support AllAnalytics.com, a sponsored third-party community. This paper explores the use of SAS software, including SAS^® Enterprise Guide^®, SAS^® Customer Experience Analytics, and SAS^® Marketing Automation to enable marketers to have better insight, better targeting, and better response from SAS programs.

At last you found it, the perfect software solution to meet the demands of your business. Now, how do you convince your company and its leaders to spend the money to bring it in-house and reap the business benefit? How do you build a compelling business justification? How should you think-through, communicate, and overcome this common business roadblock? Join Scott Sanders, a business and IT veteran who has effectively handled this challenge on numerous occasions. Hear some of his best practices and review some of his effective methods of approach, as he goes through his personal experience in building a business case and shepherding it through to final approval.

The Affordable Care Act (ACA) contains provisions that have stimulated interest in analytics among health care providers, especially those provisions that address quality of outcomes. High Impact Technologies (HIT) has been addressing these issues since before passage of the ACA and has a Health Care Data Model recognized by Gartner and implemented at several health care providers. Recently, HIT acquired SAS^® Visual Analytics, and this paper reports our successful efforts to use SAS Visual Analytics for visually exploring Big Data for health care providers. Health care providers can suffer significant financial penalties for readmission rates above a certain threshold and other penalties related to quality of care. We have been able to use SAS Visual Analytics, coupled with our experience gained from implementing the HIT Healthcare Data Model at a number of Healthcare providers, to identify clinical measures that are significant predictors for readmission. As a result, we can help health care providers reduce the rate of 30-day readmissions.

Inference of variance components in linear mixed effect models (LMEs) is not always straightforward. I introduce and describe a flexible SAS^® macro (%COVTEST) that uses the likelihood ratio test (LRT) to test covariance parameters in LMEs by means of the parametric bootstrap. Users must supply the null and alternative models (as macro strings), and a data set name. The macro calculates the observed LRT statistic and then simulates data under the null model to obtain an empirical p-value. The macro also creates graphs of the distribution of the simulated LRT statistics. The program takes advantage of processing accomplished by PROC MIXED and some SAS/IML^® functions. I demonstrate the syntax and mechanics of the macro using three examples.

The use of Cohen s kappa has enjoyed a growing popularity in the social sciences as a way of evaluating rater agreement on a categorical scale. The kappa statistic can be calculated as Cohen first proposed it in his 1960 paper or by using any one of a variety of weighting schemes. The most popular among these are the linear weighted kappa and the quadratic weighted kappa. Currently, SAS^® users can produce the kappa statistic of their choice through PROC FREQ and the use of relevant AGREE options. Complications arise however when the data set does not contain a completely square cross-tabulation of data. That is, this method requires that both raters have to have at least one data point for every available category. There have been many solutions offered for this predicament. Most suggested solutions include the insertion of dummy records into the data and then assigning a weight of zero to those records through an additional class variable. The result is a multi-step macro, extraneous variable assignments, and potential data integrity issues. The author offers a much more elegant solution by producing a segment of code which uses brute force to calculate Cohen s kappa as well as all popular variants. The code uses nested PROC SQL statements to provide a single conceptual step which generates kappa statistics of all types even those that the user wishes to define for themselves.

A case control study is in its most basic form comparing a case series to a matched control series and are commonly implemented in the field of public health. While matching is intended to eliminate confounding, the main potential benefit of matching in case control studies is a gain in efficiency. There are many known methods for selecting potential match or matches (in case of 1:n studies) per case, the most prominent being distance-based approach and matching on propensity scores. In this paper, we will go through both and compare their results and will present a macro capable of performing both.

This paper demonstrates the new case-level residuals in the CALIS procedure and how they differ from classic residuals in structural equation modeling (SEM). Residual analysis has a long history in statistical modeling for finding unusual observations in the sample data. However, in SEM, case-level residuals are considerably more difficult to define because of 1) latent variables in the analysis and 2) the multivariate nature of these models. Historically, residual analysis in SEM has been confined to residuals obtained as the difference between the sample and model-implied covariance matrices. Enhancements to the CALIS procedure in SAS/STAT^® 12.1 enable users to obtain case-level residuals as well. This enables a more complete residual and influence analysis. Several examples showing mean/covariance residuals and case-level residuals are presented.

Often in a clinical trial, measures are needed to describe pain, discomfort, or physical constraints that are visible but not measurable through lab tests or other vital signs. In these cases, researchers turn to questionnaires to provide documentation of improvement or statistically meaningful change in support safety and efficacy hypotheses. For example, in studies (like Parkinson s studies) where pain or depression are serious non-motor symptoms of the disease, these questionnaires provide primary endpoints for analysis. Questionnaire data presents unique challenges in both collection and analysis in the world of CDISC standards. The questions are usually aggregated into scale scores, as the underlying questions by themselves provide little additional usefulness. SAS^® is a powerful tool for extraction of the raw data from the collection databases and transposition of columns into a basic data structure in SDTM, which is vertical. The data is then processed further as per the instructions in the Statistical Analysis Plan (SAP). This involves translation of the originally collected values into sums, and the values of some questions need to be reversed. Missing values can be computed as means of the remaining questions. These scores are then saved as new rows in the ADaM (analysis-ready) data sets. This paper describes the types of questionnaires, how data collection takes place, the basic CDISC rules for storing raw data in SDTM, and how to create analysis data sets with derived records using ADaM standards, while maintaining traceability to the original question.

Usually, log files are checked by users only when SAS^® completes the execution of programs. If SAS finds any errors in the current line, it skips the current step and executes the next line. The process is completed only at the execution complete program. There are a few programs that will take more than a day to complete. In this case, the user opens the log file in Read-Only mode frequently to check for errors, warnings, and unexpected notes and terminates the execution of the program manually if any potential messages are identified. Otherwise, the user will be notified with the errors in the log file only at the end of the execution. Our suggestion is to run the parallel utility program along with the production program to check the log file of the currently running program and to notify the user through an e-mail when an error, warning, or unexpected note is found in the log file. Also, the execution can be terminated automatically and the user can be notified when potential messages are identified.

The life of a SAS^® program can be broken down into sets of changes made over time. Programmers are generally focused on the future, but when things go wrong, a look into the past can be invaluable. Determining what changes were made, why they were made, and by whom can save both time and headaches. This paper discusses version control and the current options available to SAS^® Enterprise Guide^® users. It then highlights the upcoming Program History feature of SAS Enterprise Guide. This feature enables users to easily track changes made to SAS programs. Properly managing the life cycle of your SAS programs will enable you to develop with peace of mind.

Can clustering discharge records improve a predictive model s overall fit statistic? Do the predictors differ across segments? This talk describes the methods and results of data mining pediatric IBD patient records from my Analytics 2013 poster. SAS^® Enterprise Miner^™ 12.1 was used to segment patients and model important predictors for the length of hospital stay using discharge records from the national Kid s Inpatient Database. Profiling revealed that patient segments were differentiated by primary diagnosis, operating room procedure indicator, comorbidities, and factors related to admission and disposition of patient. Cluster analysis of patient discharges improved the overall average square error of predictive models and distinguished predictors that were unique to patient segments.

When a SAS^® user asked for help scanning words in textual data and then matching them to pre-scored keywords, it struck a chord with SAS programmers! They contributed code that solved the problem using hash structures, SQL, informats, arrays, and PRX routines. Of course, the next question was which program is fastest! This paper compares the different approaches and evaluates the performance of the programs on varying amounts of data. The code for each program is provided to show how SAS has a variety of tools available to solve common problems. While this won t make you an expert on any of these programming techniques, you ll see each of them in action on a common problem.

The graphical display of the individual data is important in understanding the raw data and the relationship between the variables in the data set. You can explore your data to ensure statistical assumptions hold by detecting and excluding outliers if they exist. Since you can visualize what actually happens to individual subjects, you can make your conclusions more convincing in statistical analysis and interpretation of the results. SAS^® provides many tools for creating graphs of individual data. In some cases, multiple tools need to be combined to make a specific type of graph that you need. Examples are used in this paper to show how to create graphs of individual data using the SAS^® ODS Graphics procedures (SG procedures).

This paper describes a method that uses some simple SAS^® macros and SQL to merge data sets containing related data that contains rows with varying effective date ranges. The data sets are merged into a single data set that represents a serial list of snapshots of the merged data, as of a change in any of the effective dates. While simple conceptually, this type of merge is often problematic when the effective date ranges are not consecutive or consistent, when the ranges overlap, or when there are missing ranges from one or more of the merged data sets. The technique described was used by the Fairfax County Human Resources Department to combine various employee data sets (Employee Name and Personal Data, Personnel Assignment and Job Classification, Personnel Actions, Position-Related data, Pay Plan and Grade, Work Schedule, Organizational Assignment, and so on) from the County's SAP-HCM ERP system into a single Employee Action History/Change Activity file for historical reporting purposes. The technique currently is used to combine fourteen data sets, but is easily expandable by inserting a few lines of code using the existing macros.

Missing data commonly occurs in medical, psychiatry, and social researches. The SAS^® MI and MIANALYZE procedures are often used to generate multiple imputations and then provide valid statistical inferences based on them. However, MIANALYZE is not applicable to combine type-III analyses obtained using multiple imputed data sets. In this manuscript, we write a macro to combine the type-III analyses generated from the SAS MIXED procedure based on multiple imputations. The proposed method can be extended to other procedures reporting type-III analyses, such as GENMOD and GLM.

Graphic software users are confronted with what I call Options Over-Choice, and with defaults that are designed to easily give you a result, but not necessarily the best result. This presentation and paper focus on guidelines for communication-effective data visualization. It demonstrates their practical implementation, using graphic examples likely to be adaptable to your own work. Code is provided for the examples. Audience members will receive the latest update of my tip sheet compendium of graphic design principles. The examples use SAS^® tools (traditional SAS/GRAPH^® or the newer ODS graphics procedures that are available with Base SAS^®), but the design principles are really software independent. Come learn how to use data visualization to inform and influence, to reveal and persuade, using tips and techniques developed and refined over 34 years of working to get the best out of SAS^® graphic software tools.

There has been debate regarding which method to use to analyze repeated measures continuous data when the design includes only two measurement times. Five different techniques can be applied and give similar results when there is little to no correlation between pre- and post-test measurements and when data at each time point are complete: 1) analysis of variance on the difference between pre- and post-test, 2) analysis of covariance on the differences between pre- and post-test controlling for pre-test, 3) analysis of covariance on post-test controlling for pre-test, 4) multiple analysis of variance on post- test and pre-test, and 5) repeated measures analysis of variance. However, when there is missing data or if a moderate to high correlation between pre- and post-test measures exists under an intent-to-treat analysis framework, bias is introduced in the tests for the ANOVA, ANCOVA, and MANOVA techniques. A comparison of Type III sum of squares, F-tests, and p-values for a complete case and an intent-to-treat analysis are presented. The analysis using a complete case data set shows that all five methods produce similar results except for the repeated measures ANOVA due to a moderate correlation between pre- and post-test measures. However, significant bias is introduced for the tests using the intent-to-treat data set.

With the growth in size and complexity of organizations investing in SAS^® platform technologies, the size and complexity of ETL subsystems and data integration (DI) jobs is growing at a rapid rate. Developers are pushed to come up with new and innovative ways to improve process efficiency in their DI jobs to meet increasingly demanding service level agreements (SLAs). The ability to conditionally execute or switch paths in a DI job is an extremely useful technique for improving process efficiency. How can a SAS^® Data Integration developer design a job to best suit conditional execution? This paper discusses a technique for providing a parameterized dynamic execution custom transformation that can be easily incorporated into SAS^® Data Integration Studio jobs to provide process path switching capabilities. The aim of any data integration task is to ensure that all sources of business data are integrated as efficiently as possible. It is concerned with the repurposing of data via transformation, should be a value-adding process, and also should be the product of collaboration. Modularization of common or repeatable processes is a fundamental part of the collaboration process in DI design and development. Switch path a custom transformation built to conditionally execute branches or nodes in SAS Data Integration Studio provides a reusable module for solving the conditional execution limitations of standard SAS Data Integration Studio transformations and jobs. Switch Path logic in SAS Data Integration Studio can serve many purposes in day-to-day business needs for a SAS data integration developer as it is completely reusable

People from all over the world are using SAS^® analytics to achieve great things, such as to develop life-saving medicines, detect and prevent financial fraud, and ensure the survival of endangered species. Chris Hemedinger is not one of those people. Instead, Chris has used SAS to optimize his baby name selections, evaluate his movie rental behavior, and analyze his Facebook friends. Join Chris as he reviews some of his personal triumphs over the little problems in life, and learn how these exercises can help to hone your skills for when it really matters.

Non-cognitive assessments, which measure constructs such as time management, goal-setting, and personality, are becoming more prevalent today in research within the domains of academic performance and workforce readiness. Many instruments that are used for this purpose contain a large number of items that can each be assigned to specific facets of the larger construct. The factor structure of each instrument emerges from a mixture of psychological theory and empirical research, often by doing exploratory factor analysis (EFA) using the SAS^® procedure PROC FACTOR. Once an initial model is established, it is important to perform confirmatory factor analysis (CFA) to confirm that the hypothesized model provides a good fit to the data. If outcome data such as grades are collected, structural equation modeling (SEM) should also be employed to investigate how well the assessment predicts these measures. This paper demonstrates how the SAS procedure PROC CALIS is useful for performing confirmatory factor analysis and structural equation modeling. Examples of these methods are demonstrated and proper interpretation of the fit statistics and resulting output is illustrated.

If you have an existing SAS^® Business Intelligence environment and you want to add SAS^® Visual Analytics, you need to make some architectural choices. SAS Visual Analytics and SAS Business Intelligence can share certain components, such as a SAS^® Metadata Server and the SAS^® Web Infrastructure Platform. Sharing metadata eliminates the need to create and maintain duplicate information, and it enables your users to take advantage of functionality that can be shared between SAS Visual Analytics and SAS Business Intelligence. Sharing the SAS Web Infrastructure Platform enables SAS middle-tier applications such as SAS^® Visual Analytics Services and SAS^® Web Report Studio to communicate with each other. Intended for SAS architects and administrators, this paper explores supported architecture for SAS Visual Analytics and SAS Business Intelligence. The paper then identifies areas where the architecture can be shared as well as where resources should be kept separate. In addition, the paper offers recommendations and other considerations to keep in mind when you are managing shared resources.

The big questions in consumer research lead to statistical methods appropriate to them. 'What do consumers say?' is all about analyzing surveys and finding relationships between preferences and background attributes. 'What do consumers think? is about looking at higher-level structures like preference mappings that can be derived from ratings. 'What will consumers pay?' is about conducting choice experiments to pin down the way consumers trade off among features and with prices, with the willingness to pay. 'How do you trigger purchases?' is about experiments that determine which interventions work, and how to target them to potential consumers, with uplift modeling. The SAS product JMP^® version 11 was released last fall with a new group of modeling tools to address these and other questions in consumer research. Traditionally JMP has specialized in engineering tools, but consumer research is an important part of engineering, in product planning, to make sure you produce the products with the attributes consumers want.

The CDISC Study Data Tabulation Model (SDTM) provides a standardized structure and specification for a broad range of human and animal study data in pharmaceutical research, and is widely adopted in the industry for the submission of the clinical trial data. Because SDTM requires additional variables and datasets that are not normally available in the clinical database, further programming is required to convert the clinical database into the SDTM datasets. This presentation introduces the concept and general requirements of SDTM, and the different approaches in the SDTM data conversion process. The author discusses database design considerations, implementation procedures, and SAS^® macros that can be used to maximize the efficiency of the process. The creation of the metadata DEFINE.XML and the final STDM dataset validation are also discussed.

The INTCK function is used to obtain the number of time intervals between two dates. The INTCK function comes with arguments and argument-modifiers to enable us to perform a variety of date-related manipulations. This paper deals with a real-time simple usage of the INTCK function to calculate frequency of days of the week between the start and end day of a trip. The INTCK function with its arguments can directly calculate the number of days of the week as illustrated in this paper. The same usage of the INTCK function using PROC SQL is also presented in this paper. All the codes executed and presented in this paper involve Base SAS^® Release 9.3 only.

SAS^® Visual Analytics Designer enables you to create reports with different layouts. There are several basic graph objects that you can include in these reports. What if you wanted to create a report that wasn't possible with one of the out-of-the-box graph objects? No worries! The new SAS^® Visual Analytics Graph Builder available from the SAS^® Visual Analytics home page lets you create a custom graph object using built-in sample data. You can then include these graph objects in SAS Visual Analytics Designer and generate reports using them. Come see how you can create custom graph objects such as stock plots, butterfly charts, and more. These custom objects can be easily shared with others for use in SAS Visual Analytics Designer.

When submitting clinical data to the Food and Drug Administration (FDA), besides the usual trials results, we need to submit the information that helps the FDA to understand the data. The FDA has required the CDISC Case Report Tabulation Data Definition Specification (Define-XML), which is based on the CDISC Operational Data Model (ODM), for submissions using Study Data Tabulation Model (SDTM). Electronic submission to the FDA is therefore a process of following the guidelines from CDISC and FDA. This paper illustrates how to create an FDA guidance compliant define.xml v2 from metadata by using SAS^®.

The Census Bureau conducts the Common Core of Data surveys for the National Center for Education Statistics annually. We have written SAS^® programs to automate the database documentation. We try to avoid including hard-coded values in the programs. Thanks to a record layout spreadsheet, the analysts can quickly update the survey metadata outside the SAS programs. This paper explains how SAS can read the record layout spreadsheet to create formats on the fly. The analysts can update the values as changes occur over time without having to worry about writing correct SAS syntax. Behind the scenes, SAS is using dictionary views, macros, ODS OUTPUT, PROC TEMPLATE, PROC FORMAT, the ODS Report Writing Interface, and RTF to create the desired results. This paper uses syntax for SAS^® 9.2, written for programmers at the intermediate level.

LaTeX is a free document creation package that is often used to create journal articles. It provides the capability to create very specific formatting and to write a wide variety of formulas. Using ODS, SAS^® can write documents to a LaTeX file, which can then be compiled through LaTeX into PDF files. This paper briefly reviews the basic syntax and options to produce these files. Then, we look at how to create a new tagset to make changes to the standard ODS LaTeX templates to create the non-gridded table appearance that is typically seen in journal articles. We also explore how to write special characters and equations not otherwise available through ODS LaTeX.

This presentation explains how to use Base SAS^®9 software to create multi-sheet Microsoft Excel workbooks. You learn step-by-step techniques for quickly and easily creating attractive multi-sheet Excel workbooks that contain your SAS^® output using the ExcelXP ODS tagset. The techniques can be used regardless of the platform on which your SAS software is installed. You can even use them on a mainframe! Creating and delivering your workbooks on-demand and in real time using SAS server technology is discussed. Although the title is similar to previous presentations by this author, this presentation contains new and revised material not previously presented.
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The first table in many research journal articles is a statistical comparison of demographic traits across study groups. It might not be exciting, but it s necessary. And although SAS^® calculates these numbers with ease, it is a time-consuming chore to transfer these results into a journal-ready table. Introducing the time-saving deluxe %MAKETABLE SAS macro it does the heavy work for you. It creates a Microsoft Word table of up to four comparative groups reporting t-tests, chi-square, ANOVA, or median test results, including a p-value. You specify only a one-line macro call for each line in the table, and the macro takes it from there. The result is a tidily formatted journal-ready Word table that you can easily include in a manuscript, report, or Microsoft PowerPoint presentation. For statisticians and researchers needing to summarize group comparisons in a table, this macro saves time and relieves you from the drudgery of trying to make your output neat and pretty. And after all, isn t that what we want computing to do for us?

Patient safety in a neonatal intensive care unit (NICU) as in any hospital unit is critically dependent on appropriate staffing. We used SAS^® Simulation Studio to create a discrete-event simulation model of a specific NICU that can be used to predict the number of nurses needed per shift. This model incorporates the complexities inherent in determining staffing needs, including variations in patient acuity, referral patterns, and length of stay. To build our model, the group first estimated probability distributions for the number and type of patients admitted each day to the unit. Using both internal and published data, the team also estimated distributions for various NICU-specific patient morbidities, including type and timing of each morbidity event and its temporal effect on a patient s acuity. We then built a simulation model that samples from these input distributions and simulates the flow of individual patients through the NICU (consisting of critical-care and step-down beds) over a one-year time period. The general basis of our model represents a method that can be applied to any unit in any hospital, thereby providing clinicians and administrators with a tool to rigorously and quantitatively support staffing decisions. With additional refinements, the use of such a model over time can provide significant benefits in both patient safety and operational efficiency.

Business Intelligence platforms provide a bridge between expert data analysts and decision-makers and other end-users. But what do you do when you can identify no system that meets both your needs and your budget? If you are the Consolidated Data Analysis Center in the HHS Office of Inspector General, you use SAS^® Enterprise BI Server and the SAS^® Stored Process Web Application to build your own. This presentation covers the inception, design, and implementation of the PAYment by Geographic Area (PAYGAR) system, which uses only SAS^® Enterprise BI tools, namely the SAS Stored Process Web Application, PROC GMAP, and HTML/JAVA embedded in a DATA step, to create an interactive platform for presenting and exploring data that has a geographic component. In particular, the presentation reviews how we created a system of chained stored processes to enable a user to select the data to be presented, navigate through different geographic levels, and display companion reports related to the current data and geographic selections. It also covers the creation of the HTML front-end that sits over and manages the system. Throughout, the presentation emphasizes the scalability of PAYGAR, which the SAS Stored Process Web Application facilitates.

Energy companies that operate in a highly regulated environment and are constrained in pricing flexibility must employ a multitude of approaches to maintain high levels of customer satisfaction. Many investor-owned utilities are just starting to embrace a customer-centric business model to improve the customer experience and hold the line on costs while operating in an inflationary business setting. Faced with these challenges, it is natural for utility executives to ask: 'What drives customer satisfaction, and what is the optimum balance between influencing customer perceptions and improving actual process performance in order to be viewed as a top-tier performer by our customers?' J.D. Power, for example, cites power quality and reliability as the top influencer of overall customer satisfaction. But studies have also shown that customer perceptions of reliability do not always match actual reliability experience. This apparent gap between actual and perceived performance raises a conundrum: Should the utility focus its efforts and resources on improving actual reliability performance or would it be better to concentrate on influencing customer perceptions of reliability? How can this conundrum be unraveled with an analytically driven approach? In this paper, we explore how the design of experiment techniques can be employed to help understand the relationship between process performance and customer perception, thereby leading to important insights into the energy customer equation and higher customer satisfaction!

In this new era of healthcare reform, health insurance companies have heightened their efforts to pinpoint who their customers are, what their characteristics are, what they look like today, and how this impacts business in today s and tomorrow s healthcare environment. The passing of the Healthcare Reform policies led insurance companies to focus and prioritize their projects on understanding who the members in their current population were. The goal was to provide an integrated single view of the customer that could be used for retention, increased market share, balancing population risk, improving customer relations, and providing programs to meet the members' needs. By understanding the customer, a marketing strategy could be built for each customer segment classification, as predefined by specific attributes. This paper describes how SAS^® was used to perform the analytics that were used to characterize their insured population. The high-level discussion of the project includes regression modeling, customer segmentation, variable selection, and propensity scoring using claims, enrollment, and third-party psychographic data.

Merging or joining data sets is an integral part of the data consolidation process. Within SAS^®, there are numerous methods and techniques that can be used to combine two or more data sets. We commonly think that within the DATA step the MERGE statement is the only way to join these data sets, while in fact, the MERGE is only one of numerous techniques available to us to perform this process. Each of these techniques has advantages, and some have disadvantages. The informed programmer needs to have a grasp of each of these techniques if the correct technique is to be applied. This paper covers basic merging concepts and options within the DATA step, as well as a number of techniques that go beyond the traditional MERGE statement. These include fuzzy merges, double SET statements, and the use of key indexing. The discussion will include the relative efficiencies of these techniques, especially when working with large data sets.

The Washington D.C. aqueduct was completed in 1863, carrying desperately needed clean water to its many residents. Just as the aqueduct was vital and important to its residents, a lifeline if you will, so too is the supply of data to the business. Without the flow of vital information, many businesses would not be able to make important decisions. The task of building my company s first dashboard was brought before us by our CIO; the business had not asked for it. In this poster, I discuss how we were able to bring fresh ideas and data to our business units by converting the data they saw on a daily basis in reports to dashboards. The road to success was long with plenty of struggles from creating our own business requirements to building data marts, synching SQL to SAS^®, using information maps and SAS^® Enterprise Guide^® projects to move data around, all while dealing with technology and other I.T. team roadblocks. Then on to designing what would become our real-time dashboards, fighting for SharePoint single sign-on, and, oh yeah, user adoption. My story of how dashboards revitalized the business is a refreshing tale for all levels.

Cross-visit checks are a vital part of data cleaning for longitudinal studies. The nature of longitudinal studies encourages repeatedly collecting the same information. Sometimes, these variables are expected to remain static, go away, increase, or decrease over time. This presentation reviews the na ve and the better approaches at handling one-variable and two-variable consistency checks. For a single-variable check, the better approach features the new ALLCOMB function, introduced in SAS^® 9.2. For a two-variable check, the better approach uses the .first pseudo-class to flag inconsistencies. This presentation will provide you the tools to enhance your longitudinal data cleaning process.

With increased concern about privacy and simultaneous pressure to make survey data available, statistical disclosure control (SDC) treatments are performed on survey microdata to reduce disclosure risk prior to dissemination to the public. This situation is all the more problematic in the push to provide data online for immediate user query. Two SDC approaches are data coarsening, which reduces the information collected, and data swapping, which is used to adjust data values. Data coarsening includes recodes, top-codes and variable suppression. Challenges related to creating a SAS^® macro for data coarsening include providing flexibility for conducting different coarsening approaches, and keeping track of the changes to the data so that variable and value labels can be assigned correctly. Data swapping includes selecting target records for swapping, finding swapping partners, and swapping data values for the target variables. With the goal of minimizing the impact on resulting estimates, challenges for data swapping are to find swapping partners that are close matches in terms of both unordered categorical and ordered categorical variables. Such swapping partners ensure that enough change is made to the target variables, that data consistency between variables is retained, and that the pool of potential swapping partners is controlled. An example is presented using each algorithm.

Having data that are consistent, reliable, and well linked is one of the biggest challenges faced by financial institutions. The paper describes how the SAS^® Data Management offering helps to connect people, processes, and technology to deliver consistent results for data sourcing and analytics teams, and minimizes the cost and time involved in the development life cycle. The paper concludes with best practices learned from various enterprise data initiatives.

A revolution is taking place in the U.S. at both the national and state level in the area of health care transparency. Large amounts of data on the health of communities, the quality of health care providers, and the cost of health care is being collected and is being made available by both levels of government to a variety of stakeholders. The surfacing of this data and the consumption of it by health care decision makers unfolds a new opportunity to view, explore, and analyze health care data in novel ways. Furthermore, this data provides the health care system an opportunity to advance the achievement of the Triple Aim. Data transparency will bring a sea change to the world of health care by necessitating new ways of communicating information to end users such as payers, providers, researchers, and consumers of health care. This paper examines the information needs of public health care payers such as Medicare and Medicaid, and discusses the convergence of health care and data visualization in creating consumable health insights that will aid in achieving cost containment, quality improvement, and increased accessibility for populations served. Moreover, using claims data and SAS^® Visual Analytics, it examines how data visualization can help identify the most critical insights necessary to managing population health. If health care payers can analyze large amounts of claims data effectively, they can improve service and care delivery to their recipients.

Euramax is a global manufacturer of precoated metals who relies on analytics and data visualization for its decision making. Euramax has deployed significant innovations in recent years. SAS^® Visual Analytics fits in the innovative culture of Euramax and its need for information-based decision making. During this presentation, Peter Wijers shares best practices of the implementation process and several application areas.

We used OPTNET to link hedge fund datasets from four vendors, covering overlapping populations, but with no universal identifier. This quick tip shows how to treat data records as nodes, use pairwise identifiers to generate distance measures, and get PROC OPTNET to assign clusters of records from all sources to each hedge fund. This proved to be far faster, and easier, than doing the same task in PROC SQL.

Debugging SAS^® code contained in a macro can be frustrating because the SAS error messages refer only to the line in the SAS log where the macro was invoked. This can make it difficult to pinpoint the problem when the macro contains a large amount of SAS code. Using a macro that contains one small DATA step, this paper shows how to use the MPRINT and MFILE options along with the fileref MPRINT to write just the SAS code generated by a macro to a file. The 'de-macroified' SAS code can be easily executed and debugged.

Your company s chronically overloaded SAS^® environment, adversely impacted user community, and the resultant lackluster productivity have finally convinced your upper management that it is time to upgrade to a SAS^® grid to eliminate all the resource problems once and for all. But after the contract is signed and implementation begins, you as the SAS administrator suddenly realize that your company-wide standard mode of SAS operations, that is, using the traditional SAS^® Display Manager on a server machine, runs counter to the expectation of the SAS grid your users are now supposed to switch to SAS^® Enterprise Guide^® on a PC. This is utterly unacceptable to the user community because almost everything has to change in a big way. If you like to play a hero in your little world, this is your opportunity. There are a number of things you can do to make the transition to the SAS grid as smooth and painless as possible, and your users get to keep their favorite SAS Display Manager.

The evolution of the mobile landscape has created a shift in the workforce that now favors mobile devices over traditional desktops. Considering that today's workforce is not always in the office or at their desks, new opportunities have been created to deliver report content through innovative mobile experiences. SAS^® Mobile BI for both iOS and Android tablets compliments the SAS^® Visual Analytics offering by providing anytime, anywhere access to reports containing information that consumers need. This paper presents best practices and tips on how to optimize reports for mobile users, taking into consideration the constraints of limited screen real estate and connectivity, as well as answers a few frequently asked questions. Discover how SAS Mobile BI captures the power of mobile reporting to prepare for the vast growth that is predicted in the future.

New innovative, analytical techniques are necessary to extract patterns in big data that have temporal and geo-spatial attributes. An approach to this problem is required when geo-spatial time series data sets, which have billions of rows and the precision of exact latitude and longitude data, make it extremely difficult to locate patterns of interest The usual temporal bins of years, months, days, hours, and minutes often do not allow the analyst to have control of the precision necessary to find patterns of interest. Geohashing is a string representation of two-dimensional geometric coordinates. Time hashing is a similar representation, which maps time to preserve all temporal aspects of the date and time of the data into a one-dimensional set of data points. Geohashing and time hashing are both forms of a Z-order curve, which maps multidimensional data into single dimensions and preserves the locality of the data points. This paper explores the use of a multidimensional Z-order curve, combining both geohashing and time hashing, that is known as geo-temporal hashing or space-time boxes using SAS^®. This technique provides a foundation for reducing the data into bins that can yield new methods for pattern discovery and detection in big data.

Very often, there is a need to present the analysis output from SAS^® through web applications. On these occasions, it would make a lot of difference to have highly interactive charts over static image charts and graphs. Not only this is visually appealing, with features like zooming, filtering, etc., it enables consumers to have a better understanding of the output. There are a lot of charting libraries available in the market which enable us to develop cool charts without much effort. Some of the packages are Highcharts, Highstock, KendoUI, and so on. They are developed in JavaScript and use the latest HTML5 components, and they also support a variety of chart types such as line, spline, area, area spline, column, bar, pie, scatter, angular gauges, area range, area spline range, column range, bubble, box plot, error bars, funnel, waterfall, polar chart types etc. This paper demonstrates how we can combine the data processing and analytic powers of SAS with the visualization abilities of these charting libraries. Since most of them consume JSON-formatted data, the emphasis is on JSON producing capabilities of SAS, both with PROC JSON and other custom programming methods. The example would show how easy it is to do develop a stored process which produces JSON data which would be consumed by the charting library with minimum change to the sample program.

This paper outlines the techniques that I have used with my clients over the last five years to build powerful applications that run from a web browser. The user interface is presented using HTML and JavaScript, which is generated by SAS^® Stored Processes. A JavaScript framework called Ext JS is used to build components such as tables and graphs, which have a lot of functionality built in. A range of SAS^® macros are used for building HTML and JavaScript, so the generation of the user interface is simplified. This technique has been used to create a medical monitoring system, the UK Census MIS, and a bank's risk management application. I also discuss some techniques involved with integrating a system like this with SAS^® Portal, cubes, and web reports.

Particle swarm optimization is a heuristic global optimization method that was given by James Kennedy and Russell C. Eberhart in 1995. (James Kennedy and Russell C. Eberhart). The purpose of this paper develops a code for particle swarm optimization in SAS^® 9.2.

Evaluation of the efficacy of an intervention is often complicated because the intervention is not randomly assigned. Usually, interventions in marketing, such as coupons or retention campaigns, are directed at customers because their spending is below some threshold or because the customers themselves make a purchase decision. The presence of nonrandom assignment of the stimulus can lead to over- or underestimating the value of the intervention. This can cause future campaigns to be directed at the wrong customers or cause the impacts of these effects to be over- or understated. This paper gives a brief overview of selection bias, demonstrates how selection in the data can be modeled, and shows how to apply some of the important consistent methods of estimating selection models, including Heckman's two-step procedure, in an empirical example. Sample code is provided in an appendix.

New York City boasts a wide variety of cuisine owing to the rich tourism and the vibrant immigrant population. The quality of food and hygiene maintained at the restaurants serving different cuisines has a direct impact on the people dining in them. The objective of this paper is to build a model that predicts the grade of the restaurants in New York City. It also provides deeper statistical insights into the distribution of restaurants, cuisine categories, grades, criticality of violations, etc., and concludes with the sequence analysis performed on the complete set of violations recorded for the restaurants at different time periods over the years 2012 and 2013. The data for 2013 is used to test the model. The data set consists of 15 variables that capture to restaurant location-specific and violation details. The target is an ordinal variable with three levels, A, B, and C, in descending order of the quality representation. Various SAS^® Enterprise Miner^™ models, logistic regression, decision trees, neural networks, and ensemble models are built and compared using validation misclassification rate. The stepwise regression model appears to be the best model, with prediction accuracy of 75.33%. The regression model is trained at step 3. The number of critical violations at 8.5 gives the root node for the split of the target levels, and the rest of the tree splits are guided by the predictor variables such as number of critical and non-critical violations, number of critical violations for the year 2011, cuisine group, and the borough.

APP is an unofficial collective abbreviation for the SAS^® functions ADDR, PEEK, PEEKC, the CALL POKE routine, and their so-called LONG 64-bit counterparts the SAS tools designed to directly read from and write to physical memory in the DATA step. APP functions have long been a SAS dark horse. First, the examples of APP usage in SAS documentation amount to a few technical report tidbits intended for mainframe system programming, with nary a hint how the functions can be used for data management programming. Second, the documentation note on the CALL POKE routine is so intimidating in tone that many potentially receptive folks might decide to avoid the allegedly precarious route altogether. However, little can stand in the way of an inquisitive SAS programmer daring to take a close look, and it turns out that APP functions are very simple and useful tools! They can be used to explore how things really work, to make code more concise, to implement en masse data movement, and they can often dramatically improve execution efficiency. The author and many other SAS experts (notably Peter Crawford, Koen Vyverman, Richard DeVenezia, Toby Dunn, and the fellow masked by his 'Puddin' Man' sobriquet) have been poking around the SAS APP realm on SAS-L and in their own practices since 1998, occasionally letting the SAS community at large to peek at their findings. This opus is an attempt to circumscribe the results in a systematic manner. Welcome to the APP world! You are in for a few glorious surprises.

The health-care industry in the United States is going through a paradigm shift moving away from its focus on treating diseases and toward promoting health, wellness, and preventive public health programs, so that both the individuals and the government can maintain a healthy bottom line. The high-level business problem is to reduce the expected medical costs and number of medical services required by the people of New Hampshire by implementing successful disease prevention programs. The objective is to identify which among the six prevention programs will successfully improve the health of the residents of New Hampshire over nine future years (2012 2020). The business scenario of the case is to identify the preventive programs that are most effective in reducing the costs in New Hampshire and to invest the money in those programs so that the overall health-care overhead costs can be reduced or controlled. The effectiveness of implementing the preventive programs was evaluated using SAS^® Enterprise Guide^® 5.1 and SAS^® Enterprise Miner^™ 12. Time series analysis, in particular, forecasting, is used to project the future health-care services and costs for the years from 2012 to 2020. Our analysis showed that all the preventive programs should be implemented concurrently. The minimum anticipated savings in cost is approximately $572,111 or 3.3% of the expected baseline cost of $17,297,931. Therefore, our recommendation is to use this cost reduction figure, $572,111, as the initial funding investment toward initiating the six prevention programs concurrently, so that tangible results can be noticed by 2020.

There certainly is no shortage of hype when it comes to the term 'Big Data' as vendors and enterprises alike highlight the transformative effect of building actionable insight from the deluge of data that is now available to us all. But among the hype, practical guidance is often lacking: why is Apache Hadoop most often the technology underpinning 'Big Data'? How does it fit into the current landscape of databases and data warehouses that are already in use? Are there typical usage patterns that can be used to distill some of the inherent complexity for us all to speak a common language? And if there are common patterns, what are some ways that I can apply them to my unique situation? This session has the following agenda: Learn what types of data are being captured to build 'Big Data' applications Discover where Hadoop most often fits into the data landscape for the typical enterprise Hear how common patterns of use can simplify your approach and help you to find a usage that makes sense for your business See how other organizations have used the usage patterns to get started on their Big Data journey

'NOTE: No unequal values were found. All values compared are exactly equal.' Do your eyes automatically drop to the end of your PROC COMPARE output in search of these words? Do you then conclude that your data sets match? Be careful here! Major discrepancies might still lurk in the shadows, and you ll never know about them if you make this common mistake. This paper describes several of PROC COMPARE s blind spots and how to steer clear of them. Watch in horror as PROC COMPARE glosses over important differences while boldly proclaiming that all is well. See the gruesome truth about what PROC COMPARE does, and what it doesn t do! Learn simple techniques that allow you to peer into these blind spots and avoid getting blindsided by PROC COMPARE!

Response rates, churn models, customer lifetime value today's marketing departments are more analytically driven than ever. Marketers have had their heads down developing analytic capabilities for some time. The results have been game-changing. But it's time for marketers to look up and discover which analytic results from other departments can enhance the analytics of marketing. What if you knew the demand forecast for your products? What could you do? What if you understood the price sensitivity for your products? How would this impact the actions that your marketing team takes? Using the hospitality industry as an example, we explore how marketing teams can use the analytic outputs from other departments to get better results overall.

Vistaprint saw the opportunity in the printing market to get more out of high-volume printing by grouping similar orders in large groups. They heavily rely on technology to handle design, printing, and order handling and use the Internet as a medium. With their successful expansion across the world, the issue they were facing was a lot of one-time buyers and a lot of registered users who didn't finish the check-out. The need to implement a retention strategy was the next logical step, for which they chose SAS^® Campaign Management. In this session, Vistaprint explains how they use campaign management for retention and how the project was addressed. They will also touch on how the concept of high performance could open up new possibilities for them.

Ebony and Ivory was a number one song by Paul McCartney and Stevie Wonder about making music together, proper integration, unity, and harmony on a deeper level. With SAS^® Visual Analytics, current Enterprise Business Intelligence (BI) customers can rest assured that their years of existing BI work and content can coexist until they can fully transition over to SAS Visual Analytics. This presentation covers 10 inter-operability integration points between SAS^® BI and SAS Visual Analytics.

Both recent banking and insurance risk regulations require effective aggregation of risks. To determine the total enterprise risk for a financial institution, all risks must be aggregated and analyzed. Typically, there are two approaches: bottom-up and top-down risk aggregation. In either approach, financial institutions face challenges due to various levels of risks with differences in metrics, data source, and availability. First, it is especially complex to aggregate risk. A common view of the dependence between all individual risks can be hard to achieve. Second, the underlying data sources can be updated at different times and can have different horizons. This in turn requires an incremental update of the overall risk view. Third, the risk needs to be analyzed across on-demand hierarchies. This paper presents SAS^® solutions to these challenges. To address the first challenge, we consider a mixed approach to specify copula dependence between individual risks and allow step-by-step specification with a minimal amount of information. Next, the solution leverages an event-driven architecture to update results on a continuous basis. Finally, the platform provides a self-service reporting and visualization environment for designing and deploying reports across any hierarchy and granularity on the fly. These capabilities enable institutions to create an accurate, timely, comprehensive, and adaptive risk-aggregation and reporting system.

With the introduction of new features in SAS^® 9.4 Grid Manager, administrators of SAS solutions have even better capabilities for effectively managing the use of SAS^® Enterprise Guide^® in a grid environment. In this paper, we explain and demonstrate proven practices for configuring the SAS 9.4 Grid Manager environment, leveraging grid options sets and grid-spawned SAS^® Workspace Servers. We walk through the options provided by SAS Enterprise Guide that make the most effective use of the grid environment.

The implicit loop refers to the DATA step repetitively reading data and creating observations, one at a time. The explicit loop, which uses the iterative DO, DO WHILE, or DO UNTIL statements, is used to repetitively execute certain SAS^® statements within each iteration of the DATA step execution. Explicit loops are often used to simulate data and to perform a certain computation repetitively. However, when an explicit loop is used along with array processing, the applications are extended widely, which includes transposing data, performing computations across variables, and so on. To be able to write a successful program that uses loops and arrays, one needs to know the contents in the program data vector (PDV) during the DATA step execution, which is the fundamental concept of DATA step programming. This workshop covers the basic concepts of the PDV, which is often ignored by novice programmers, and then illustrates how to use loops and arrays to transform lengthy code into more efficient programs.

Literature suggests two main approaches, parametric and non-parametric, for constructing efficiency frontiers on which efficiency scores of other units can be based. Parametric functions can be either deterministic or stochastic in nature. However, when multiple inputs and outputs are encountered, Data Envelopment Analysis (DEA), a non-parametric approach, is a powerful tool used for decades in measurement of productivity/efficiency with a wide range of applications. Both approaches have advantages and limitations. This paper attempts to further explore and validate a hybrid approach, taking the best of both the DEA and the parametric approach, in order to estimate efficiency of Decision Making Units (DMUs) in an even better way.

Data quality depends on review by operational stewards of the content. Volumes of complex data disappear as e-mail attachments. Is there a critical data shift that might be missed? Embedding a summary image drives expert data review from 15% to 87%. Downstream error rate is significantly reduced. Increased accuracy to variable physician compensation measures results.

The availability of specialized programming and analysis resources in academic medical centers is often limited, creating a significant challenge for clinical research. The current work describes how Base SAS^® and SAS^® Enterprise Guide^® are being used to empower research staff so that they are less reliant on these scarce resources.

This session provides an overview of how SAS^® environments can be best integrated with SAP HANA. You learn what is different about SAP HANA and how SAS users can access and push-down their work, and thus start to benefit from the in-memory power of SAP HANA. We further highlight how the SAS^® Predictive Modeling Workbench embeds in the SAP HANA platform and the value this co-innovation delivers to you.

Typically, it takes a system administrator to understand the graphic data results that are generated in the Microsoft Windows Performance Monitor. However, using SAS/GRAPH^® software, you can customize performance results in such a way that makes the data easier to read and understand than the data that appears in the default performance monitor graphs. This paper uses a SAS^® data set that contains a subset of the most common performance counters to show how SAS programmers can create an improved, easily understood view of the key performance counters by using SAS/GRAPH software. This improved view can help your organization reduce resource bottlenecks on systems that range from large servers to small workstations. The paper begins with a concise explanation of how to collect data with Windows Performance Monitor. Next, examples are used to illustrate the following topics in detail: converting and formatting a subset of the performance-monitor data into a data set using a SAS program to generate clearly labeled graphs that summarize performance results analyzing results in different combinations that illustrate common resource bottlenecks

For data analysts, one of the most important steps after manipulating and analyzing the data set is to create a report for it. Nowadays, many statistics tables and reports are generated as HTML files that can be easily accessed through the Internet. However, the SAS^® Output Delivery System (ODS) HTML output has many limitations on interacting with users. In this paper, we introduce a method to enhance the traditional ODS HTML output by using jQuery (a JavaScript library). A macro was developed to implement this idea. Compared to the standard HTML output, this macro can add sort, pagination, search, and even dynamic drilldown function to the ODS HTML output file.

Pipeline parallelism, an extension of MP Connect, is an effective way to speed processing. Piping allows the typical programming sequence of DATA step followed by PROC to execute in parallel. Piping uses TCP ports to pass records directly from the DATA step to the PROC immediately as each individual record is processed. The DATA step in effect becomes a data transformation filter for the PROC , running in parallel and incurring no additional disk storage or related I/O lag. Establishing a pipe with MP Connect typically requires specifying a physical TCP port to be used by the writing and by the reading processes. Coding in this style opens the possibility for users to generate systems conflicts by inadvertently requesting ports that are in use. SAS^® Metadata Server allows one to allocate ports dynamically; that is, users can use a symbolic name for the port with the server dynamically determining an unused port to temporarily assign to the SAS^® job. While this capability is attractive, implementing SAS Metadata Server on a system which does not use any of the other SAS BI technology can be inefficient from a cost perspective. To enable dynamic port allocation without the added cost, we created a UNIX script which can be called from within SAS to ascertain which ports are available at runtime. The script returns a list of available ports which is captured in a SAS macro variable and subsequently used in establishing pipeline parallelism.

This paper is based on the belief that debugging your programs is not only necessary, but also a good way to gain insight into how SAS^® works. Once you understand why you got an error, a warning, or a note, you'll be better able to avoid problems in the future. In other words, people who are good debuggers are good programmers. This paper covers common problems including missing semicolons and character-to-numeric conversions, and the tricky problem of a DATA step that runs without suspicious messages but, nonetheless, produces the wrong results. For each problem, the message is deciphered, possible causes are listed, and how to fix the problem is explained.

In evaluation instruments and tests, individual items are often collected using an ordinal measurement or Likert type scale. Typically measures such as Cronbach s alpha are estimated using the standard Pearson correlation. Gadderman and Zumbo (2012) illustrate how using the standard Pearson correlations may yield biased estimates of reliability when the data are ordinal and present methodology for using the polychoric correlation in reliability estimates as an alternative. This session shows how to implement the methods of Gadderman and Zumbo using SAS^® software. An example will be presented that incorporates these methods in the estimation of the reliability of an active learning post-occupancy evaluation instrument developed by Steelcase Education Solutions researchers.

The worst part of going to school is having to show up. However, data shows that those who do show up are the ones that are going to be the most successful (Johnson, 2000). As shown in a study done in Minneapolis, students who were in class at least 95% of the time were twice as likely pass state tests (Johnson, 2000). Studies have been conducted and show that school districts that show interest in attendance have higher achievement in students (Reeves, 2008). The goal in doing research on student attendance is to find out the patterns of when people are missing class and why they are absent. The data comes directly from the Phillip O Berry High School Attendance Office, with around 1600 students; there is plenty of data to be used from the 2012 2013 school year. Using Base SAS^® 9.3, after importing the data in from Microsoft Excel, a series of PROC formats and PROC GCharts were used to output and analyze the data. The data showed the days of the week and period that students missed the most, depending on grade level. The data shows that Freshman and Seniors were the most likely to be absent on a given day. Based on the data, attendance continues to be a issue; therefore, school districts need to take an active role in developing attendance policies.

Gartner claims the 'Internet of Things' trend will add 50 billion connected devices by 2015 on top of the 2 billion connected people who currently populate the Internet as we know it. Understanding what is happening in these environments is a huge challenge because the flow and volume of data is ever increasing. And while the types of data processing itself do not change, where you do this processing, how event streams are captured, and how important events are defined does. Event stream processing is important technology for capturing, analyzing, and processing fast flowing data in motion. This session will give you an overview of where SAS is going in support of event streaming.

Given a time series data set, you can use automatic time series modeling software to select an appropriate time series model. You can use various statistics to judge how well each candidate model fits the data (in-sample). Likewise, you can use various statistics to select an appropriate model from a list of candidate models (in-sample or out-of-sample or both). Finally, you can use rolling simulations to evaluate ex-ante forecast performance over several forecast origins. This paper demonstrates how you can use SAS^® Forecast Server Procedures and SAS^® Forecast Studiosoftware to perform the statistical analyses that are related to rolling simulations.

Logistic regression is a powerful technique for predicting the outcome of a categorical response variable and is used in a wide range of disciplines. Until recently, however, this methodology was available only for data that were collected using a simple random sample. Thanks to the work of statisticians such as Binder (1983), logistic modeling has been extended to data that are collected from a complex survey design that includes strata, clusters, and weights. Through examples, this paper provides guidance on how to use PROC SURVEYLOGISTIC to apply logistic regression modeling techniques to data that are collected from a complex survey design. The examples relate to calculating odds ratios for models with interactions, scoring data sets, and producing ROC curves. As an extension of these techniques, a final example shows how to fit a Generalized Estimating Equations (GEE) logit model.

SAS^® is an outstanding suite of software, but not everyone in the workplace speaks SAS. However, almost everyone speaks Excel. Often, the data you are analyzing, the data you are creating, and the report you are producing is a form of a Microsoft Excel spreadsheet. Every year at SAS^® Global Forum, there are SAS and Excel presentations, not just because Excel isso pervasive in the workplace, but because there s always something new to learn (or re-learn)! This paper summarizes and references (and pays homage to!) previous SAS Global Forum presentations, as well as examines some of the latest Excel capabilities with the latest versions of SAS^® 9.4 and SAS^® Visual Analytics.

Business Intelligence (BI) dashboards serve as an invaluable, high-level, visual reference tool for decision-making processes in many business industries. A request was made to our department to develop some BI dashboards that could be incorporated in an academic setting. These dashboards would aim to serve various undergraduate executive and administrative staff at the university. While most business data may lend itself to work very well and easily in the development of dashboards, academic data is typically modeled differently and, therefore, faces unique challenges. In this paper, the authors detail and share the design and development process of creating dashboards for decision making in an academic environment utilizing SAS^® BI Dashboard 4.3 and other SAS^® Enterprise Business Intelligence 9.2 tools. The authors also provide lessons learned as well as recommendations for future implementations of BI dashboards utilizing academic data.

Explore the various DATA step merge and PROC SQL join processes. This presentation examines the similarities and differences between merges and joins, and provides examples of effective coding techniques. Attendees examine the objectives and principles behind merges and joins, one-to-one merges (joins), and match-merge (equi-join), as well as the coding constructs associated with inner and outer merges (joins) and PROC SQL set operators.

Potential of One, Power of All. That has a really nice ring to it, especially as it pertains to accessing all of your corporate data through one single data access point. It means the potential of having a single source for all of your data connections from throughout the enterprise. It also means that the complexities of connecting to these data assets from the various source systems throughout the enterprise are hidden from the end user. With this, however, comes the possibility of placing personally identifiable information in the hands of a user who should not have access to it. The bottom line is that there is risk and uncertainty with allowing users to have access to data that is disallowed by your existing data governance strategy. Blocking these data elements from specific users or groups of users is a challenge that many corporations face today, whether it is secure financial information, confidential personnel records, or personal medical information protected by strict regulations. How do you surface All necessary data to All necessary users, while at the same time maintaining the security of the data? SAS^® Federation Server Manager is an easy-to-use interface that allows the data administrator to manage your data assets in such a way that it alleviates this risk by controlling access to critical data elements and maintaining the proper level of data disclosure control. This session focuses on how to employ various data access control strategies from within SAS Federation Server Manager.

SAS^® can easily perform calculations and export the result to Microsoft Excel in a report. However, sometimes you need Excel to have a formula or a function in a cell and not just a number. Whether it s for a boss who wants to see a SUM formula in the total cell or to have automatically updating reports that can be sent to people who don t use SAS to be completed, exporting formulas to Excel can be very powerful. This paper illustrates how, by using PROC REPORT and PROC PRINT along with the ExcelXP tagset, you can easily export formulas and functions into Excel directly from SAS. The method outlined in this paper requires Base SAS^® 9.1 or higher and Excel 2002 or later and requires a basic understanding of the ExcelXP tagset.

The growing adoption of electronic systems for keeping medical records provides an opportunity for health care practitioners and biomedical researchers to access traditionally unstructured data in a new and exciting way. Pathology reports, progress notes, and many other sections of the patient record that are typically written in a narrative format can now be analyzed by employing natural language processing contextual extraction techniques to identify specific concepts contained within the text. Linking these concepts to a standardized nomenclature (for example, SNOMED CT, ICD-9, ICD-10, and so on) frees analysts to explore and test hypotheses using these observational data. Using SAS^® software, we have developed a solution in order to extract data from the unstructured text found in medical pathology reports, link the extracted terms to biomedical ontologies, join the output with more structured patient data, and view the results in reports and graphical visualizations. At its foundation, this solution employs SAS^® Enterprise Content Categorization to perform entity extraction using both manually and automatically generated concept definition rules. Concept definition rules are automatically created using technology developed by SAS, and the unstructured reports are scored using the DS2/SAS^® Content Categorization API. Results are post-processed and added to tables compatible with SAS^® Visual Analytics, thus enabling users to visualize and explore data as required. We illustrate the interrelated components of this solution with examples of appropriate use cases and describe manual validation of performance and reliability with metrics such as precision and recall. We also provide examples of reports and visualizations created with SAS Visual Analytics.

When you want to know the details about a small subset of a much larger data set, it can take a long time to select the records you need. This paper shows you how to create a user-defined SAS^® format to pull only the observations that you want out of a big data source. Even when selecting a million records out of data sets that can have more than 100 million records, this method is much quicker than either a PROC SQL join or a SAS merge.

Each month, our project team delivers updated 5-Star ratings for 15,700+ nursing homes across the United States to Centers for Medicare and Medicaid Services. There is a wealth of data (and processing) behind the ratings, and this data is longitudinal in nature. A prior paper in this series, 'Programming the Provider Previews: Extreme SAS^® Reporting,' discussed one aspect of the processing involved in maintaining the Nursing Home Compare website. This paper will discuss two other aspects of our processing: creating an annual data Compendium and extending the 5-star processing to accommodate several different output formats for different purposes. Products used include Base SAS^®, SAS/STAT^®, ODS Graphics procedures, and SAS/GRAPH^®. New annotate facilities in both SAS/GRAPH and the ODS Graphics procedures will be discussed. This paper and presentation will be of most interest to SAS programmers with medium to advanced SAS skills.

The continued expansion of governance associated with the Supervisory Guidance on Model Risk Management (OCC 2011-12, SR 11-7), which is published by the Office of the Comptroller of the Currency and the Board of Governors of the Federal Reserve System, now includes all areas within a financial institution, including scenarios associated with financial crimes compliance. There is now an expectation to track, monitor, and audit the overall scenario management through the entire cycle. This includes authoring scenarios, managing changes associated with those scenarios (what if scenarios, champion and challenger scenarios, etc.), promoting those scenarios to production, and the ongoing measuring and monitoring of those scenarios. Leveraging the power of SAS^® Visual Scenario Designer, we can execute all of these tasks that facilitate interaction between the modeling group that manages the scenarios and the data associated through case investigation. This paper discusses how to use SAS^® Visual Analytics, SAS Visual Scenario Designer, and SAS^® Financial Crimes Suite to converge traditional business operations approaches and to develop, test, and promote models to allow for greater control and tracking for the compliance groups.

Traditionally, web applications interact with back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the growing need for real-time charting and complex analysis types of data representation on these web applications, SAS computing power can be put to use by adding a SAS web service layer between the application and the database. With the experience that we have with integrating these applications to SAS^® BI Web Services, this is our attempt to point out five things to do when using SAS BI Web Services. 1) Input Data Sources: always enable Allow rewinding stream while creating the stored process. 2) Use LIBNAME statements to define XML filerefs for the Input and Output Streams (Data Sources). 3) Define input prompts and output parameters as global macro variables in the stored process if the stored process calls macros that use these parameters. 4) Make sure that all of the output parameters values are set correctly as defined (data type) before the end of the stored process. 5) The Input Streams (if any) should have a consistent data type; essentially, every instance of the stream should have the same structure. This paper consist of examples and illustrations of errors and warnings associated with the previously mentioned cases.

Data is often stored in highly normalized ( tall and skinny ) structures that are not convenient for analysis. The SAS^® programmer frequently needs to transform the data to arrange relevant variables together in a single row. Sometimes this is a simple matter of using the TRANSPOSE procedure to flip the values of a single variable into separate variables. However, when there are multiple variables to be transposed to a single row, it might require multiple transpositions to obtain the desired result. This paper describes five different ways to achieve this flip-flop, explains how each method works, and compares the usefulness of each method in various situations. Emphasis is given to achieving a data-driven solution that minimizes hard-coding based on prior knowledge of the possible values each variable can have and that improves maintainability and reusability of the code. The intended audience is novice and intermediate SAS programmers who have a basic understanding of the DATA step and the TRANSPOSE procedure.

No way. Not gonna happen. I am a real SAS^® programmer. (Spoken by a Real SAS Programmer.) SAS^® Enterprise Guide^® sometimes gets a bad rap. It was originally promoted as a code generator for non-programmers. The truth is, however, that SAS Enterprise Guide has always allowed programmers to write their own code. In addition, it offers many features that are not included in PC SAS^®. This presentation shows you the top ten features that people who like to write code care about. It will be taught by a programmer who now prefers using SAS Enterprise Guide.

For almost two decades, Western Kentucky University's Office of Institutional Research (WKU-IR) has used SAS^® to help shape the future of the institution by providing faculty and administrators with information they can use to make a difference in the lives of their students. This presentation provides specific examples of how WKU-IR has shaped the policies and practices of our institution and discusses how WKU-IR moved from a support unit to a key strategic partner. In addition, the presentation covers the following topics: How the WKU Office of Institutional Research developed over time; Why WKU abandoned reactive reporting for a more accurate, convenient system using SAS^® Enterprise Intelligence Suite for Education; How WKU shifted from investigating what happened to predicting outcomes using SAS^® Enterprise Miner^™ and SAS^® Text Miner; How the office keeps the system relevant and utilized by key decision makers; What the office has accomplished and key plans for the future.

In this interconnected world, it is becoming ever more important to understand not just details about your data, but also how different parts of your data are related to each other. From social networks to supply chains to text analytics, network analysis is becoming a critical requirement and network visualization is one of the best ways to understand the results. The new SAS^® Visual Analytics network visualization shows links between related nodes as well as additional attributes such as color, size, or labels. This paper explains the basic concepts of networks as well as provides detailed background information on how to use network visualizations within SAS Visual Analytics.

Based on selection criteria, the SAS^® Data Integration Studio loop or splitter transformations can be used to generate multiple output files. The ETL developer or SAS^® administrator can decide which transformation is better suited for the design, priorities, and SAS configuration at their site. Factors to consider are the setup, maintenance, and performance of the ETL job. The loop transformation requires an understanding of macros and a control table. The splitter transformation is more straightforward and self documenting. If time allows, creating and running a job with each transformation can provide benchmarking to measure performance. For a comparison of these two options, this paper shows an example of the same job using the loop or splitter transformation. For added testing metrics, one can adapt the LOGPARSE SAS macro to parse the job logs.

PROC TABULATE is the most widely used reporting tool in SAS^®, along with PROC REPORT. Any kind of report with the desired statistics can be produced by PROC TABULATE. When we need to report some summary statistics like mean, median, and range in the heading, either we have to edit it outside SAS in word processing software or enter it manually. In this paper, we discuss how we can automate this to be dynamic by using PROC SQL and some simple macros.

This paper shares our experience integrating two leading data analytics and Geographic Information Systems (GIS) software products SAS^® and ArcGIS to provide integrated reporting capabilities. SAS is a powerful tool for data manipulation and statistical analysis. ArcGIS is a powerful tool for analyzing data spatially and presenting complex cartographic representations. Combining statistical data analytics and GIS provides increased insight into data and allows for new and creative ways of visualizing the results. Although products exist to facilitate the sharing of data between SAS and ArcGIS, there are no ready-made solutions for integrating the output of these two tools in a dynamic and automated way. Our approach leverages the individual strengths of SAS and ArcGIS, as well as the report delivery infrastructure of SAS^® Information Delivery Portal.

This introductory presentation is intended for an audience new to mixed models who wants to get an overview of this useful class of models. Learn about mixed models as an extension of ordinary regression models, and see several examples of mixed models in social, agricultural, and pharmaceutical research.

For decades, mixed models have been used by researchers to account for random sources of variation in regression-type models. Now, they are gaining favor in business statistics for giving better predictions for naturally occurring groups of data, such as sales reps, store locations, or regions. Learn about how predictions based on a mixed model differ from predictions in ordinary regression and see examples of mixed models with business data.

When the dependent variable is a count, Poisson regression is a natural choice of distribution for fitting a regression model. This presentation is intended for an audience experienced in linear regression modeling, but new to Poisson regression modeling. Learn the basics of this useful distribution and see some examples where it is appropriate. Tips for identifying problems with fitting a Poisson regression model and some helpful alternatives are provided.

Analyzing data from a complex probability survey involves weighting observations so that inferences are correct. This introductory presentation is intended for an audience new to analyzing survey data. Learn the essentials of using the SURVEYxx procedures in SAS/STAT^®.

Do you need a statistic that is not computed by any SAS^® procedure? Reach for the SAS/IML^® language! Many statistics are naturally expressed in terms of matrices and vectors. For these, you need a matrix-vector language. This hands-on workshop introduces the SAS/IML language to experienced SAS programmers. The workshop focuses on statements that create and manipulate matrices, read and write data sets, and control the program flow. You will learn how to write user-defined functions, interact with other SAS procedures, and recognize efficient programming techniques. Programs are written using the SAS/IML^® Studio development environment. This course covers Chapters 2 4 of Statistical Programming with SAS/IML Software (Wicklin, 2010).

Have you ever seen SAS^® Visual Analytics reports that are somehow more elegant than a standard report? Which qualities make reports easier to navigate, more appealing to the eye, or reveal insights more quickly? These quick tips will reveal several SAS Visual Analytics report design characteristics to help make your reports stand out from the pack. We cover concepts like color palettes, content organization, interactions, labeling, and branding, just to name a few.

With the ever increasing proliferation of disparate complex data being collected and stored, it has never been more important that this information is accurate, clean, integrated, and often times in compliance with an expanding set of government regulations. This means that the data must be cleaned and standardized, duplicates must be identified and removed, and the individual data must be able to be joined or merged together in some way. However, it is often the case that this data does not have the same variables or values to make this possible with a simple Join or Merge. To that end, one has to employ a set of fuzzy logics or fuzzy matching. Simply put, fuzzy matching is the implementation of algorithmic processes (fuzzy logic) to determine the similarity between elements of data such as business names, people names, or address information. Fuzzy logic is used to predict the probability of data with non-exact matches to help in data cleansing, deduplication, or matching of disparate data sets. This paper shows the basics of using fuzzy logic by using SAS^® functions, COMPLEV, multiple variables matches, and a modified Porter stemming algorithm.

It is not uncommon to find models with random components like location, clinic, teacher, etc., not just the single error term we think of in ordinary regression. This paper uses several examples to illustrate the underlying ideas. In addition, the response variable might be Poisson or binary rather than normal, thus taking us into the realm of generalized linear mixed models, These too will be illustrated with examples.

Beginning with SA^®S 9.2, ODS Graphics introduces a whole new way of generating graphs using SAS^®. With just a few lines of code, you can create a wide variety of high-quality graphs. This paper covers the three basic ODS Graphics procedures SGPLOT, SGPANEL, and SGSCATTER. SGPLOT produces single-celled graphs. SGPANEL produces multi-celled graphs that share common axes. SGSCATTER produces multi-celled graphs that might use different axes. This paper shows how to use each of these procedures in order to produce different types of graphs, how to send your graphs to different ODS destinations, how to access individual graphs, and how to specify properties of graphs, such as format, name, height, and width.

This paper illustrates some SAS^® graphs that can be useful for variable selection in predictive modeling. Analysts are often confronted with hundreds of candidate variables available for use in predictive models, and this paper illustrates some simple SAS graphs that are easy to create and that are useful for visually evaluating candidate variables for inclusion or exclusion in predictive models. The graphs illustrated in this paper are bar charts with confidence intervals using the GCHART procedure and comparative histograms using the UNIVARIATE procedure. The graphs can be used for most combinations of categorical or continuous target variables with categorical or continuous input variables. This paper assumes the reader is familiar with the basic process of creating predictive models using multiple (linear or logistic) regression.

Missing data is an ever-present issue, and analysts should exercise proper care when dealing with it. Depending on the data and the analytical approach, this problem can be addressed by simply removing records with missing data. However, in most cases, this is not the best approach. In fact, this can potentially result in inaccurate or biased analyses. The SAS^® programming language offers many DATA step processes and functions for handling missing values. However, some analysts might not like or be comfortable with programming. Fortunately, SAS^® Enterprise Guide^® can provide those analysts with a number of simple built-in tasks for discovering missing data and diagnosing their distribution across fields. In addition, various techniques are available in SAS Enterprise Guide for imputing missing values, varying from simple built-in tasks to more advanced tasks that might require some customized SAS code. The focus of this presentation is to demonstrate how SAS Enterprise Guide features such as Query Builder, Filter and Sort Wizard, Describe Data, Standardize Data, and Create Time Series address missing data issues through the point-and-click interface. As an example of code integration, we demonstrate the use of a code node for more advanced handling of missing data. Specifically, this demonstration highlights the power and programming simplicity of PROC EXPAND (SAS/ETS^® software) in imputing missing values for time series data.

Have you ever needed additional data that was only accessible via a web service in XML or JSON? In some situations, the web service is set up to only accept parameter values that return data for a single observation. To get the data for multiple values, we need to iteratively pass the parameter values to the web service in order to build the necessary dataset. This paper shows how to combine the SAS^® hash object with the FILEVAR= option to iteratively pass a parameter value to a web service and input the resulting JSON or XML formatted data.

SAS^® provides some powerful, flexible tools for creating reports, like PROC REPORT and PROC TABULATE. With the advent of the Output Delivery System (ODS), you have almost total control over how the output from those procedures looks. But there are still times when you need (or want) just a little more, and that s where the Report Writing Interface (RWI) can help. The RWI is just a fancy way of saying that you are using the ODSOUT object in a DATA step. This object enables you to lay out the page, create tables, embed images, add titles and footnotes, and more all from within a DATA step, using whatever DATA step logic you need. Also, all the style capabilities of ODS are available to you so that the output created by your DATA step can have fonts, sizes, colors, backgrounds, and borders that make your report look just like you want. This presentation quickly covers some of the basics of using the ODSOUT object and then walks through some of the techniques to create four real-world examples. Who knows, you might even go home and replace some of your PROC REPORT code I know I have!

Healthcare services data on products and services come in different shapes and forms. Data cleaning, characterization, massaging, and transformation are essential precursors to any statistical model-building efforts. In addition, data size, quality, and distribution influence model selection, model life cycle, and the ease with which business insights are extracted from data. Analysts need to examine data characteristics and determine the right data transformation and methods of analysis for valid interpretation of results. In this presentation, we demonstrate the common data distribution types for a typical healthcare services industry such as Cardinal Health and their salient features. In addition, we use Base SAS^® and SAS/STAT^® for data transformation of both the response (Y) and the explanatory (X) variables in four combinations [RR (Y and X as row data), TR (only Y transformed), RT (only X transformed), and TT (Y and X transformed)] and the practical significance of interpreting linear, logistic, and completely randomized design model results using the original and the transformed data values for decision-making processes. The reality of dealing with diverse forms of data, the ramification of data transformation, and the challenge of interpreting model results of transformed data are discussed. Our analysis showed that the magnitude of data variability is an overriding factor to the success of data transformation and the subsequent tasks of model building and interpretation of model parameters. Although data transformation provided some benefits, it complicated analysis and subsequent interpretation of model results.

When first presented with SAS^® Enterprise Guide^®, many existing SAS^® programmers don't know where to begin. They want to understand, 'What's in it for me?' if they switch over. These longtime users of SAS are accustomed to typing all of their code into the Program Editor window and clicking Submit. This beginning tutorial introduces SAS Enterprise Guide 6.1 to old and new users of SAS who need to code. It points out advantages and tips that demonstrate why a user should be excited about the switch. This tutorial focuses on the key points of a session involving coding and introduces new features. It covers the top three items for a user to consider when switching over to a server-based environment. Attendees will return to the office with a new motivation and confidence to start coding with SAS Enterprise Guide.

A group tasked with testing SAS^® software from the customer perspective has gathered a number of helpful hints for SAS^® 9.4 that will smooth the transition to its new features and products. These hints will help with the 'huh?' moments that crop up when you're getting oriented and will provide short, straightforward answers. And we can share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure you are ready to begin your transition to SAS^® 9.4.

Do you create reports via a mainframe? If so, you can use SAS^® as a one-stop shop for all of your data manipulations. SAS can efficiently read data, create data sets without the need for multiple DATA steps, and produce Excel reports without the need for edits. This poster helps novice mainframe programmers by providing helpful tips to efficiently create reports using SAS in the mainframe environment. Topics covered are replacing JCL with SAS for reading data, efficient merging for efficient programming, and using PROQ FREQ for data quality and PROC TABULATE for superior reporting.

Many organizations need to forecast large numbers of time series that are organized in a hierarchical fashion. Good forecasting practices recommend that several hierarchies be used and that each hierarchy contain a homogeneous set of time series with similar statistical properties. Modeling and forecasting homogeneous time series hierarchies provide better out-of-sample forecast performance. Because an organization might have many time series hierarchies, it is often desirable to model and forecast these hierarchical time series in parallel for computational efficiency. Additionally, it is often desirable to aggregate forecasts from several nonhomogeneous time series hierarchies for report generation. This paper demonstrates these techniques for forecasting time series hierarchies in parallel and for aggregating the forecasts by using SAS^® Forecast Server and SAS^® Grid Manager.

Portfolio segmentation is key in all forecasting projects. Not all products are equally predictable. Nestl uses animal names for its segmentation, and the animal behavior translates well into how the planners should plan these products. Mad Bulls are those products that are tough to predict, if we don't know what is causing their unpredictability. The Horses are easier to deal with. Modern time series based statistical forecasting methods can tame Mad Bulls, as they allow to add explanatory variables into the models. Nestl now complements its Demand Planning solution based on SAP with predictive analytics technology provided by SAS^®, to overcome these issues in an industry that is highly promotion-driven. In this talk, we will provide an overview of the relationship Nestl is building with SAS, and provide concrete examples of how modern statistical forecasting methods available in SAS^® Demand-Driven Planning and Optimization help us to increase forecasting performance, and therefore to provide high service to our customers with optimized stock, the primary goal of Nestl 's supply chains.

This case study shows how SAS^® Enterprise Guide^® and SAS^® Enterprise BI made it possible to easily implement reports of fraud prevention in BF Financial Services and also how to help operational areas to increase efficiency through automation of information delivery. The fraud alert report was made using a program developed in SAS Enterprise Guide to detect frauds on loan applications and later published in SAS^® Web Report Studio in order to be analyzed by a team. The second example is the automation by SAS BI of a payment report that spent 30% of the time of a six-worker staff.

SAS offers advanced analytics while Teradata has developed one of the fastestdatabases known to mankind. The Office of the Actuary at CMS uses SAS andTeradata to perform many important tasks such as setting Medicare Advantagerates for providers, forecasting the cost of closing the Part-D Donut Hole, andestimating the future cost of healthcare in America. The major advantage to theTeradata platform is the speed at which data can be summarized as compared tolegacy systems (billions of rows of data can be summarized in seconds where itonce took days) and the SAS system provides many options for accessing andanalyzing this data - whether you re on a mainframe, Windows^®, or Unixthrough SAS^® Enterprise Guide or Business Intelligence.

The role of the Data Scientist is the viral job description of the decade. And like LOLcats, there are many types of Data Scientists. What is this new role? Who is hiring them? What do they do? What skills are required to do their job? What does this mean for the SAS^® programmer and the statistician? Are they obsolete? And finally, if I am a SAS user, how can I become a Data Scientist? Come learn about this job of the future and what you can do to be part of it.

Do you have data in SharePoint that you would like to run analysis on with SAS^®? This workshop teaches you how to create a custom task in SAS^® Enterprise Guide^® in order to find, retrieve, and format that data into a SAS data set for use in your SAS programs.

Recent studies suggest that unstructured data, such as customer comments or feedback, can enhance the power of existing predictive models. SAS^® Text Miner can generate singular value decomposition (SVD) units from text documents, which is a vectorial representation of terms in documents. These SVDs, when used as additional inputs along with the existing structured input variables, often prove to capture the response better. However, SVD units are sort of black box variables and are not easy to interpret or explain. This is a big hindrance to win over the decision makers in the organizations to incorporate these derived textual data components in the models. In this paper, we demonstrate a new and powerful feature in SAS^® Text Miner 12.1 that helps in explaining the SVDs or the text cluster components. We discuss two important methods that are useful to interpreting them. For this purpose, we used data from a television network company that has transcripts of its call center notes from three prior calls of each customer. We are able to extract the key terms from the call center notes in the form of Boolean rules, which have contributed to the prediction of customer churn. These rules provide an intuitive sense of which set of terms, when occurring in either the presence or absence of another set of terms in the call center notes, might lead to a churn. It also provides insights into which customers are at a bigger risk of churning from the company s services and, more importantly, why.

No matter how long you ve been programming in SAS^®, using and manipulating dates still seems to require effort. Learn all about SAS dates, the different ways they can be presented, and how to make them useful. This paper includes excellent examples for dealing with raw input dates, functions to manage dates, and outputting SAS dates into other formats. Included is all the date information you will need: date and time functions, Informats, formats, and arithmetic operations.

Retail price setting is influenced by two distinct factors: the regular price and the promotion price. Together, these factors determine the list price for a specific item at a specific time. These data are often reported only as a singular list price. Separating this one price into two distinct prices is critical for accurate price elasticity modeling in retail. These elasticities are then used to make sales forecasts, manage inventory, and evaluate promotions. This paper describes a new time-series feature extraction utility within SAS^® Forecast Server that allows for automated separation of promotional and regular prices.

The Healthcare and Life Sciences industry is by nature conservative and tends to change slowly. Driven by the joint needs to improve quality and lower costs for their services and products that rate of change is rapidly increasing. We are now able to take data in almost any format; organize it, curate it, and turn it into actionable knowledge. We can now do this in real time and make a meaningful difference in citizen s lives. From genomics (all the omics actually), to operations, to clinical care, to health and wellness, Big data is popping up everywhere. From ICD-10 CAC (Computer Assisted Coding) to clinical trials big data is there. With the rapid advancement in remote monitoring (RPM), the Internet of Things (IoT), and personalized clinical medicine (PCM), changes in drug discovery, care delivery and disease prevention are rapidly moving from concept to mainstream practice. Come hear about the IT infrastructure and capability your organization needs to develop, to deliver on such capabilities. Whether you are a researcher, a practicing clinician, Govt official or hospital administrator you will need to understand and use big data solutions to thrive in the coming era of healthcare and big data.

This presentation is for users who are familiar with SAS^® Enterprise Guide^® but might not be aware of the many useful new features added in versions 4.2 and beyond. For example, SAS Enterprise Guide allows you to: Format your SAS^® source code to make it easier to read. Easily schedule a project to run at a given time. Work with OLAP data in your enterprise. We will overview these and other features to help you become even more productive using this powerful application.

The DATA step has served SAS^® programmers well over the years, and although it is powerful, it has not fundamentally changed. With DS2, SAS has introduced a significant alternative to the DATA step by introducing an object-oriented programming environment. In this paper, we share our experiences with getting started with DS2 and learning to use it to access, manage, and share data in a scalable, threaded, and standards-based way.

Determining what, when, and how to migrate SAS^® software from one major version to the next is a common challenge. SAS provides documentation and tools to help make the assessment, planning, and eventual deployment go smoothly. We describe some of the keys to making your migration a success, including the effective use of the SAS^® Migration Utility, both in the analysis mode and the execution mode. This utility is responsible for analyzing each machine in an existing environment, surfacing product-specific migration information, and creating packages to migrate existing product configurations to later versions. We show how it can be used to simplify each step of the migration process, including recent enhancements to flag product version compatibility and incompatibility.

This paper illustrates a permutation method for implementing multiple comparisons on Pearson s Chi-square test for an R×C contingency table, using the SAS^® FREQ procedure and a newly developed SAS macro called CHISQ_MC. This method is analogous to the Tukey-type multiple comparison method for one-way analysis of variance.

Power producers are looking for ways to not only improve efficiency of power plant assets but also to grow concerns about the environmental impacts of power generation without compromising their market competitiveness. To meet this challenge, this study demonstrates the application of data mining techniques for process optimization in a coal-fired power plant in Thailand with 97,920 data records. The main purpose is to determine which factors have a great impact on both (1) heat rate (kJ/kWh) of electrical energy output and (2) opacity of the flue gas exhaust emissions. As opposed to the traditional regression analysis currently employed at the plant and based on Microsoft Excel, more complex analytical models using SAS^® Enterprise Miner^™ help supporting managerial decision to improve the overall performance of the existing energy infrastructure while reducing emissions through a change in the energy supply structure.

Detecting patterns in graphics output is much easier when numeric data can be grouped categorically. Such is the case with Body Mass Index and its four classifications: underweight, normal weight, overweight, and obese. This presentation goes from conventional histogram to asymmetric violin plot with coverage of the categorical histogram along the way. HISTOGRAM, BANDPLOT, and LATTICE statements are described in context. SAS^® 9.3 must be used to replicate the graphs.

Administrators at Western Kentucky University rely on the Institutional Research department to perform detailed statistical analyses to deepen the understanding of issues associated with enrollment management, student and faculty performance, and overall program operations. This paper presents several instances of analyses performed for the university to help it identify and recruit suitable candidates, uncover root causes in grade and enrollment trends, evaluate faculty effectiveness, and assess the impact of student characteristics, programs, or student activities on retention and graduation rates. The paper briefly discusses the data infrastructure created and used by Institutional Research. For each analysis performed, it reviews the SAS^® program and key components of the SAS code involved. The studies presented include the use of SAS^® Enterprise Miner^™ to create a retention model incorporating dozens of student background variables. It shows an examination of grade trends in the same courses taught by different faculty and subsequent student behavior and success, providing insights into the nuances and subtleties of evaluating faculty performance. Another analysis uncovers the possible influence of fraternities and sororities in freshmen algebra courses. Two investigations explore the impact of programs on student retention and graduation rates. Each example and its findings illustrate how Institutional Research can support the administration of university operations. The target audience is any SAS professional interested in learning more about Institutional Research in higher education and how SAS software is used by an Institutional Research department to serve its organization.

SAS^® High-Performance Analytics is a significant step forward in the area of high-speed, analytic processing in a scalable clustered environment. However, Big Data problems generally come with data from lots of data sources, at varying levels of maturity. Teradata s innovative Unified Data Architecture (UDA) represents a significant improvement in the way that large companies can think about Enterprise Data Management, including the Teradata Database, Hortonworks Hadoop, and Aster Data Discovery platform in a seamless integrated platform. Together, the two platforms provide business users, analysts, and data scientists with the ideally suited data management platforms, targeted specifically to their analytic needs, based upon analytic use cases, managed in a single integrated enterprise data management environment. The paper will focus on how several companies today are using Teradata s Integrated Hardware and Software UDA Platform to manage a single enterprise analytic environment, fight the ongoing proliferation of analytic data marts, and speed their operational analytic processes.

SAS^® solutions are tightly integrated with the scheduling capabilities provided by SAS^® Grid Manager and Platform Suite for SAS^®. Many organizations require that their corporate scheduler be used to control SAS processing within the enterprise. Historically this has been a laborious process, requiring duplication of job and flow information using manual forms and cumbersome change management. This paper provides proven techniques and methods that enable tight integration between the corporate scheduler and SAS without the administrative overhead. Platform Suite for SAS can be used to create flows which are then executed by the corporate scheduler. The business unit can tweak the flow without reference to the enterprise scheduling team. The approaches discussed are: Using the corporate scheduler to: Trigger SAS flows and to respond to flow return codes Restart a SAS flow that has exited due to error conditions Enable and disable LSF queues, allowing jobs that have been queued up to run within a time window that is managed on external dependencies rather than time How to configure your SAS environment to leverage the provided capabilities Real-world use cases to highlight the features and benefits of this approach The contents of this paper is of interest to SAS administrators and IT personnel responsible for enterprise scheduling. Full code and deployment instructions will be made available.

Controlled vocabularies define a common set of concepts that retain their meaning across contexts, supporting consistent use of terms to annotate, integrate, retrieve, and interpret information. Controlled vocabularies are large hierarchical structures that cannot be represented using typical SAS^® practices (e.g., SAS format statements and hash objects). This paper compares and contrasts three models for representing hierarchical structures using SAS data sets: adjacency list, path enumeration, and nested set (Celko, 2004; Mackey, 2002). Specific controlled vocabularies include a university organizational structure and several biological vocabularies (MeSH, NCBI Taxonomy, and GO). The paper presents data models and SAS code for populating tables and performing queries. The paper concludes with a discussion of implications for data warehouse implementation and future work related to efficiency of update and delete operations.

The use, limits, and misuse of statistical models in different industries are propelling new techniques and best practices in forecasting. Until recently, many factors such as data collection and storage constraints, poor data synchronization capabilities, technology limitations, and limited internal analytical expertise have made it impossible to forecast intermittent demand. In addition, integrating consumer demand data (that is, point-of-sale [POS]/syndicated scanner data from ACNielsen/ Information Resources Inc. [IRI]/Intercontinental Marketing Services [IMS]) to shipment forecasts was a challenge. This presentation gives practical how-to advice on intermittent forecasting and outlines a framework, using multi-tiered causal analysis (MTCA), that links demand to supply. The framework uses a process of nesting causal models together by using data and analytics.

This presentation addresses two main topics: The first topic focuses on the industry's norms and the best practices for building internal credit ratings (PD, EAD, and LGD). Although there is not any capital relief to local US banks using internal credit ratings (the US hs not adopted the Internal Rating Based approach of Basel2, with the exception of the top 10 banks), there is an increased responsiveness in credit ratings modeling for the last two years in the US banking industry. The main reason is the added value a bank can achieve from these ratings, and that is the focus of the second part of this presentation. It describes our journey (a client story) for getting there, introducing the SAS^® project. Even more importantly, it describes how we use credit ratings in order to achieve effective credit risk management and get real added value out of that investment. The key success factor for achieving it is to effectively implement ratings within the credit process and throughout decision making . Only then can ratings be used to improve risk-adjusted return on capital, which is the high-end objective of all of us.

Understanding the actual gambling behavior of an individual over the Internet, we develop markers which identify behavioral patterns, which in turn can be used to predict the level of risk a subscriber is prone to gambling. The data set contains 4,056 subscribers. Using SAS^® Enterprise Miner^™ 12.1, a set of models are run to predict which subscriber is likely to become a high-risk internet gambler. The data contains 114 variables such as first active date and first active product used on the website as well as the characteristics of the game such as fixed odds, poker, casino, games, etc. Other measures of a subscriber s data such as money put at stake and what odds are being bet are also included. These variables provide a comprehensive view of a subscriber s behavior while gambling over the website. The target variable is modeled as a binary variable, 0 indicating a risky gambler and 1 indicating a controlled gambler. The data is a typical example of real-world data with many missing values and hence had to be transformed, imputed, and then later considered for analysis. The model comparison algorithm of SAS Enterprise Miner 12.1 was used to determine the best model. The stepwise Regression performs the best among a set of 25 models which were run using over a 100 permutations of each model. The Stepwise Regression model predicts a high-risk Internet gambler at an accuracy of 69.63% with variables such as wk4frequency and wk3frequency of bets.

Organizations today make numerous decisions within their businesses that affect almost every aspect of their daily operations. Many of these decisions are now automatically generated by sophisticated enterprise decision management systems. These decisions include what offers to make to customers, sales transaction processing, payment processing, call center interactions, industrial maintenance, transportation scheduling, and thousands of other applications that all have a significant impact on the business bottom line. Concurrently, many of these same companies have developed or are now developing analytics that provide valuable insight into their customers, their products, and their markets. Unfortunately, many of the decision systems cannot maximize the power of analytics in the business processes at the point where the decisions are made. SAS^® Decision Manager is a new product that integrates analytical models with business rules and deploys them to operational systems where the decisions are made. Analytically driven decisions can be monitored, assessed, and improved over time. This paper describes the new product and its use and shows how models and business rules can be joined into a decision process and deployed to either batch processes or to real-time web processes that can be consumed by business applications.

This session introduces frailty models and their use in biostatistics to model time-to-event or survival data. The session uses examples to review situations in which a frailty model is a reasonable modeling option, to describe which SAS^® procedures can be used to fit frailty models, and to discuss the advantages and disadvantages of frailty models compared to other modeling options.

The NLIN procedure fits a wide variety of nonlinear models. However, some models can be so nonlinear that standard statistical methods of inference are not trustworthy. That s when you need the diagnostic and inferential features that were added to PROC NLIN in SAS/STAT^® 9.3, 12.1, and 13.1. This paper presents these features and explains how to use them. Examples demonstrate how to use parameter profiling and confidence curves to identify the nonlinearcharacteristics of the model parameters. They also demonstrate how to use the bootstrap method to study the sampling distribution of parameter estimates and to make more accurate statistical inferences. This paper highlights how measures of nonlinearity help you diagnose models and decide on potential reparameterization. It also highlights how multithreading is used to tame the large number of nonlinear optimizations that are required for these features.

Item response theory (IRT) is concerned with accurate test scoring and development of test items. You design test items to measure various types of abilities (such as math ability), traits (such as extroversion), or behavioral characteristics (such as purchasing tendency). Responses to test items can be binary (such as correct or incorrect responses in ability tests) or ordinal (such as degree of agreement on Likert scales). Traditionally, IRT models have been used to analyze these types of data in psychological assessments and educational testing. With the use of IRT models, you can not only improve scoring accuracy but also economize test administrations by adaptively using only the discriminative items. These features might explain why in recent years IRT models have become increasingly popular in many other fields, such as medical research, health sciences, quality-of-life research, and even marketing research. This paper describes a variety of IRT models, such as the Rasch model, two-parameter model, and graded response model, and demonstrates their application by using real-data examples. It also shows how to use the IRT procedure, which is new in SAS/STAT^® 13.1, to calibrate items, interpret item characteristics, and score respondents. Finally, the paper explains how the application of IRT models can help improve test scoring and develop better tests. You will see the value in applying item response theory, possibly in your own organization!

Traditional SAS^® programs typically consist of a series of SAS DATA steps, which refine input data sets until the final data set or report is reached. SAS DATA steps do not run in-database. However, SAS^® Enterprise Guide^® users can replicate this kind of iterative programming and have the resulting process flow run in-database by linking a series of SAS Enterprise Guide Query Builder tasks that output SAS views pointing at data that resides in a Teradata database, right up to the last Query Builder task, which generates the final data set or report. This session both explains and demonstrates this functionality.

Formats are an often under-valued tool in the SAS^® toolbox. They can be used in just about all domains to improve the readability of a report, or they can be used as a look-up table to recode your data. Out of the box, SAS includes a multitude of ready-defined formats that can be applied without modification to address most recode and redisplay requirements. And if that s not enough, there is also a FORMAT procedure for defining your own custom formats. This paper looks at using some of the formats supplied by SAS in some innovative ways, but primarily focuses on the techniques we can apply in creating our own custom formats.

SAS^® 9.4 has improved clustering capabilities that allow for scalability and failover for middle-tier servers and the metadata server. In this presentation, we share our experiences with high-availability and failover testing done prior to SAS 9.4 availability. We discuss what we tested and lessons learned (good and bad) while doing the testing.

Report automation and scheduling are very hot topics in many industries. They confer many advantages including reduced work load, elimination of repetitive tasks, generatation of accurate results, and better performance. This paper illustrates how to design an appropriate program to automate and schedule reports in SAS^® 9.1 and SAS^® Enterprise Guide^® 5.1 using a SAS^® server as well as the Windows Scheduler. The automation part includes good aspects of formatting Microsoft Excel tables using XML or VBA coding or any other formats, and conditional auto e-mailing with file attachments. We systematically walk through each step with a clear flow diagram from the data source to the final destination. We also discuss details of server-side and PC-side schedulers and how these schedulers involve invoking batch programs.

Despite its popularity in recent years, .NET development has yet to enjoy the quality, level, and depth of statistical support that has always been provided by SAS^®. And yet, many .NET applications could benefit greatly from the power of SAS and, likewise, some SAS applications could benefit from friendly graphical user interfaces (GUIs) supported by Microsoft s .NET Framework. What the author sets out to do here is to 1) outline the basic mechanics of automating SAS with .NET, 2) provide a framework and specific strategies for maintaining parallelism between the two platforms at runtime, and 3) sketch out put some simple applications that provide an exciting combination of powerful SAS analytics and highly accessible GUIs. The mechanics of automating SAS with .NET will be covered briefly. Attendees will learn the required objects and methods needed to pass information between the two platforms. The attendees will learn some strategies for organizing their projects and for writing SAS code that lends itself to automation. This will include embedding SAS scripts within a .NET project and managing communications between the two platforms. Specifically, the log and listing output will be captured and handled by .NET, and user actions will be interpreted and sent to the SAS engine. Example applications used throughout the session include a tool that converts between SAS variable types through simple drag-and-drop and an application that analyzes the growth of the user s computer hard drive.

Being flexible and highlighting important details in your output is critical. The use of ODS ESCAPECHAR allows the SAS^® programmer to insert inline formatting functions into variable values through the DATA step, and it makes for a quick and easy way to highlight specific data values or modify the style of the table cells in your output. What is an easier and more efficient way to concatenate those inline formatting functions to the variable values? This paper shows how the CAT functions can simplify this task.

Traditional merchandise planning processes have been primarily product and location focused, with decisions about assortment selection, breadth and depth, and distribution based on the historical performance of merchandise in stores. However, retailers are recognizing that in order to compete and succeed in an increasingly complex marketplace, assortments must become customer-centric. Advanced analytics can be leveraged to generate actionable insights into the relevance of merchandise to a retailer's various customer segments and purchase channel preferences. These insights enrich the merchandise and assortment planning process. This paper describes techniques for using advanced analytics to impact customer-centric assortments. Topics covered include approaches for scoring merchandise based on customer relevance and preferences, techniques for gaining insight into customer relevance without customer data, and an overall approach to a customer-driven merchandise planning process.

Ensemble models combine two or more models to enable a more robust prediction, classification, or variable selection. This paper describes three types of ensemble models: boosting, bagging, and model averaging. It discusses go-to methods, such as gradient boosting and random forest, and newer methods, such as rotational forest and fuzzy clustering. The examples section presents a quick setup that enables you to take fullest advantage of the ensemble capabilities of SAS^® Enterprise Miner^™ by using existing nodes, Start Groups and End Groups nodes, and custom coding.

Designing laundry products has become more complex and challenging over the years. This has occurred for many reasons: portfolio expansions, rapidly changing supply conditions, and product cost pressures to name a few. The pace of change is fast and ever-increasing. Simplifying our approach to our product portfolio is desired in order to increase our agility and enable us to react to these rapidly changing conditions. This talk will describe the application of mathematical optimization to create a more agile and productive approach for managing our product portfolio.

The soaring number of publicly available data sets across disciplines have allowed for increased access to real-life data for use in both research and educational settings. These data often leverage cost-effective complex sampling designs including stratification and clustering, which allow for increased efficiency in survey data collection and analyses. Weighting becomes a necessary component in these survey data in order to properly calculate variance estimates and arrive at sound inferences through statistical analysis. Generally speaking, these weights are included with the variables provided in the public use data, though an explanation for how and when to use these weights is often lacking. This paper presents an analysis using the California Health Interview Survey to compare weighted and non-weighted results using SAS^® PROC LOGISTIC and PROC SURVEYLOGISTIC.

There is an increasing interest in exploring healthcare practices and costs for the American working population and their dependents to improve the quality and efficiency of care and to compare healthcare performance. Comparative data is needed to evaluate and benchmark financial and clinical performance. Because of the large amounts of comparative dataavailable, it is useful to use data exploration tools. In this paper, the authors describe theirexperience building a prototype to extract data from MarketScan ResearchDatabases, load the data into SAS^® Visual Analytics, and explore this healthcaredata to understand drug adherence for a diabetes population.

U.S. educators face a critical new imperative: to prepare all students for work and civic roles in a globalized environment in which success increasingly requires the ability to compete, connect, and cooperate on an international scale. The Asia Society and the Longview Foundation are collaborating on a project to show both the need for and supply of globally competent graduates. This presentation shows you how SAS assisted these organizations with a solution that leverages SAS^® visualization technologies in order to produce a heatmap application. The heatmap application surfaces data from over 300 indicators and surfaces over a quarter million data points in a highly iterative heatmap application. The application features a drillable map that shows data at the state level as well as at the county level for all 50 states. This endeavor involves new SAS^® 9.4 technology to both combine the data and to create the interface. You'll see how SAS procedures, such as PROC JSON, which came out in SAS 9.4, were used to prepare the data for the web application. The user interface demonstrates how SAS/GRAPH^® output can be combined with popular JavaScript frameworks like Dojo and Twitter Bootstrap to create an HTML5 application that works on desktop, mobile, and tablet devices.

SAS^® Environment Manager is included with the release of SAS^® 9.4. This exciting new product enables administrators to monitor the performance and operation of their SAS^® deployments. What very few people are aware of is that the data collected by SAS Environment Manager is stored in a centralized data mart that's designed to help administrators better understand the behavior and performance of the components of their SAS solution stack. This data mart could also be used to help organizations to meet their ITIL reporting and measurement requirements. In addition to the information about alerts, events, and performance metrics collected by the SAS Environment Manager agent technology, this data mart includes the metadata audit and content usage data previously available only from the SAS^® Audit, Performance and Measurement Package.

This paper provides a set of ideas about design elements of SAS^® macros. This paper is a checklist for programmers who write or test macros.

Effective graphs are indispensable for modern statistical analysis. They reveal tendencies that are not readily apparent in simple tables and add visual clarity to reports. My client is a big graph fan; he always shows me a lot of high-quality and complex sample graphs that were created by other software and asks me Can SAS^® duplicate these outputs? Often, by leveraging the capabilities of the ODS Graph Template Language and the SGRENDER procedure, the answer is Yes . Graph Template Language offers SAS users a more direct approach to customize the output and to overlay graphs in different levels. This paper uses cases drawn from a real work situation to demonstrate how to get the seemingly unattainable results with the power of Graph Template Language: utilizing bubble plots as your distribution density bars creating refreshing looking linear regression graphics with the slop information in the legend overlaying different plots together to create sophisticated analytical bottleneck test output

If you have been programming SAS^® for years, you have probably made Display Manager your own: customized window layout, program text colors, bookmarks, and abbreviations/keyboard macros. Now you are using SAS^® Enterprise Guide^®. Did you know you can have almost all the same modifications you had in Base SAS^® in SAS Enterprise Guide, plus more?

How do you compare group responses when the data are unbalanced or when covariates come into play? Simple averages will not do, but LS-means are just the ticket. Central to postfitting analysis in SAS/STAT^® linear modeling procedures, LS-means generalize the simple average for unbalanced data and complicated models. They play a key role both in standard treatment comparisons and Type III tests and in newer techniques such as sliced interaction effects and diffograms. This paper reviews the definition of LS-means, focusing on their interpretation as predicted population marginal means, and it illustrates their broad range of use with numerous examples.

A common complaint from users working on identifying fraud and abuse in Medicare is that teams focus on operational applications, static reports, and high-level outliers. But, when faced with the need to constantly evaluate changing Medicare provider and beneficiary or enrollee dynamics, users are clamoring for more dynamic and accurate detection approaches. Providing these organizations with a data discovery and predictive analytics framework that leverages Hadoop and other big data approaches, while providing a clear path for teams to make more fact-based decisions more quickly is very important in pre- and post-fraud and abuse analysis. Organizations that do pursue a framework and a reusable services-based data discovery and analytics framework and architecture approach enjoy greater success in supporting data management, reporting, and analytics demands. They can quickly turn models into prioritized alerts and avoid improper or fraudulent payments. A successful framework should enable organizations to come up with efficient fraud, waste, and abuse models to address complex schemes; identify fraud, waste, and abuse vulnerabilities; and shorten triage efforts using a variety of data sourced from big data platforms like Hadoop and other relational database management systems. This paper talks about the data management, data discovery, predictive analytics, and social network analysis capabilities that are included in the SAS fraud framework and how a unified approach can significantly reduce the lifecycle of building and deploying fraud models. We hope this paper will provide IT leaders with a clear path for resolving issues from the simple to the incredibly complex, through a measured and scalable approach for delivering value for fraud, waste, and abuse models by providing deep insights to support evidence-based investigations.

When we start programming, we simply hope that the log comes out with no errors or warnings. Yet once we have programmed for a while, especially in the area of pharmaceutical research, we realize that having a log with specific, useful information in it improves quality and accountability. We discuss clearing the log, sending the log to an output file, helpful information to put in the log, which messages are permissible, automated log checking, adding messages regarding data changes, whether or not we want to see source code, and a few other log-related ideas. Hopefully, the log will become something that we keep in mind from the moment we start programming.

Today's business needs require 24/7 access to your data in order to perform complex queries to your analytical store. In addition, you might need to periodically update your analytical store to append new data, delete old data, or modify some existing data. Historically, the size of your analytical tables or the window in which the table must be updated can cause unacceptable downtime for queries. This paper describes how you can use new SAS^® Scalable Performance Data Server 5.1 cluster table features to simulate transaction isolation in order to modify large sections of your cluster table. These features are optimized for extremely fast operation and can be done without affecting any on-going queries. This provides both the continuous query access and periodic update requirements for your analytical store that your business model requires.

Email is an important marketing channel for digital marketers. We can stay connected with our subscribers and attract them with relevant content as long as they are still subscribed to our email communication. In this session, we are planning to discuss why it's important to manage opt-out risk; how did we predict opt-out risk; and how do we proactively manage opt-out using the models we developed.

Data governance combines the disciplines of data quality, data management, data policy management, business process management, and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS^® has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise s overall data management efficiency. This paper discusses data governance use cases and challenges, and provides an example of how to manage the data governance lifecycle to ensure success.

The capabilities of SAS^® have been extended by the use of macros and custom formats. SAS macro code libraries and custom format libraries can be stored in various locations, some of which may or may not always be easily and efficiently accessed from other operating environments. Code can be in various states of development ranging from global organization-wide approved libraries to very elementary just-getting-started code. Formalized yet flexible file structures for storing code are needed. SAS user environments range from standalone systems such as PC SAS or SAS on a server/mainframe to much more complex installations using multiple platforms. Strictest attention must be paid to (1) file location for macros and formats and (2) management of the lack of cross-platform portability of formats. Macros are relatively easy to run from their native locations. This paper covers methods of doing this with emphasis on: (a) the option sasautos to define the location and the search order for identifying macros being called, and (b) even more importantly the little-known SAS option MAUTOLOCDISPLAY to identify the location of the macro actually called in the saslog. Format libraries are more difficult to manage and cannot be created and run in a different operating system than that in which they were created. This paper will discuss the export, copying and importing of format libraries to provide cross-platform capability. A SAS macro used to identify the source of a format being used will be presented.

This paper describes a technique for calibrating street address match logic to maximize the match rate without introducing excessive erroneous matching.

One of the most common questions about logistic regression is How do I know if my model fits the data? There are many approaches to answering this question, but they generally fall into two categories: measures of predictive power (like R-squared) and goodness of fit tests (like the Pearson chi-square). This presentation looks first at R-squared measures, arguing that the optional R-squares reported by PROC LOGISTIC might not be optimal. Measures proposed by McFadden and Tjur appear to be more attractive. As for goodness of fit, the popular Hosmer and Lemeshow test is shown to have some serious problems. Several alternatives are considered.

Breast cancer is the most common cancer among females globally. After being diagnosed and treated for breast cancer, patients fear the recurrence of breast cancer. Breast cancer recurrence (BCR) can be defined as the return of breast cancer after primary treatment, and it can recur within the first three to five years. BCR studies have been conducted mostly in developed countries such as the United States, Japan, and Canada. Thus, the primary aim of this study is to investigate the feasibility of building a medical scorecard to assess the risk of BCR among Malaysian women. The medical scorecard was developed using data from 454 out of 1,149 patients who were diagnosed and underwent treatment at the Department of Surgery, Hospital Kuala Lumpur from 2006 until 2011. The outcome variable is a binary variable with two values: 1 (recurrence) and 0 (remission). Based on the availability of data, only 13 categorical predictors were identified and used in this study. The predictive performance of the Breast Cancer Recurrence scorecard (BCR scorecard) model was compared to the standard logistic regression (LR) model. Both the BCR scorecard and LR model were developed using SAS^® Enterprise Miner^™ 7.1. From this exploratory study, although the BCR scorecard model has better predictive ability with a lower misclassification rate (18%) compared to the logistic regression model (23%), the sensitivity of the BCR scorecard model is still low, possibly due to the small sample size and small number of risk factors. Five important risk factors were identified: histological type, race, stage, tumor size, and vascular invasion in predicting recurrence status.

SAS^® has a large portfolio of Java EE applications. In releases previous to SAS^® 9.4, SAS provides support for configuring, deploying, and running these applications in Oracle WebLogic, IBM WebSphere, or Red Hat JBoss. Beginning with SAS^® 9.4, SAS has updated the middle-tier architecture to deliver and run these web applications exclusivcely in the SAS^® Web Application Server (a specialized, extended configuration of Pivotal tc Server), rather than the other thrid-party web application servers. This paper discusses the motivation, technology selections, and architecture on which this change is based. It also describes the advantages that the new approach presents to customers, including increased automation of installation and configuration tasks, and improved system administration.

In applied statistical practice, incomplete measurement sequences are the rule rather than the exception. Fortunately, in a large variety of settings, the stochastic mechanism governing the incompleteness can be ignored without hampering inferences about the measurement process. While ignorability only requires the relatively general missing at random assumption for likelihood and Bayesian inferences, this result cannot be invoked when non-likelihood methods are used. We will first sketch the framework used for contemporary missing-data analysis. Apart from revisiting some of the simpler but problematic methods, attention will be paid to direct likelihood and multiple imputation. Because popular non-likelihood-based methods do not enjoy the ignorability property in the same circumstances as likelihood and Bayesian inferences, weighted versions have been proposed. This holds true in particular for generalized estimating equations (GEE). Even so-called doubly-robust versions have been derived. Apart from GEE, also pseudo-likelihood based strategies can be adapted appropriately. We describe a suite of corrections to the standard form of pseudo-likelihood, to ensure its validity under missingness at random. Our corrections follow both single and double robustness ideas, and is relatively simple to apply.

Mobile devices are taking over conventional ways of sharing and presenting information in today s businesses and working environments. Accessibility to this information is a key factor for companies and institutions in order to reach wider audiences more efficiently. SAS^® software provides a powerful set of tools that allows developers to fulfill the increasing demand in mobile reporting without needing to upgrade to the latest version of the platform. Here at University of Central Florida (UCF), we were able to create reports targeting our iPad consumers at our executive level by using the SAS^® 9.2 Enterprise Business Intelligence environment, specifically SAS^® Web Report Studio 4.3. These reports provide them with the relevant data for their decision-making process. At UCF, the goal is to provide executive consumers with reports that fit on one screen in order to avoid the need of scrolling and that are easily exportable to PDF. This is done in order to respond to their demand to be able to accomodate their increasing use of portable technology to share sensitive data in a timely manner. The technical challenge is to provide specific data to those executive users requesting access through their iPad devices. Compatibility issues arise but are successfully bypassed. We are able to provide reports that fit on one screen and that can be opened as a PDF if needed. These enhanced capabilities were requested and well received by our users. This paper presents techniques we use in order to create mobile reports.

Bootstrapped Decision Tree is a variable selection method used to identify and eliminate unintelligent variables from a large number of initial candidate variables. Candidates for subsequent modeling are identified by selecting variables consistently appearing at the top of decision trees created using a random sample of all possible modeling variables. The technique is best used to reduce hundreds of potential fields to a short list of 30 50 fields to be used in developing a model. This method for variable selection has recently become available in JMP^® under the name BootstrapForest; this paper presents an implementation in Base SAS^®9. The method does accept but does not require a specific outcome to be modeled and will therefore work for nearly any type of model, including segmentation, MCMC, multiple discrete choice, in addition to standard logistic regression. Keywords: Bootstrapped Decision Tree, Variable Selection

For most practitioners, ordinary least square (OLS) regression with a Gaussian distributional assumption might be the top choice for modeling fractional outcomes in many business problems. However, it is conceptually flawed to assume a Gaussian distribution for a response variable in the [0, 1] interval. In this paper, several modeling methodologies for fractional outcomes with their implementations in SAS^® are discussed through a data analysis exercise in predicting corporate financial leverage ratios. Various empirical and conceptual methods for the model evaluation and comparison are also discussed throughout the example. This paper provides a comprehensive survey about how to model fractional outcomes.

Predicting loss given default (LGD) is playing an increasingly crucial role in quantitative credit risk modeling. In this paper, we propose to apply mixed effects models to predict corporate bonds LGD, as well as other widely used LGD models. The empirical results show that mixed effects models are able to explain the unobservable heterogeneity and to make better predictions compared with linear regression and fractional response regression. All the statistical models are performed in SAS/STAT^®, SAS^® 9.2, using specifically PROC REG and PROC NLMIXED, and the model evaluation metrics are calculated in PROC IML. This paper gives a detailed description on how to use PROC NLMIXED to build and estimate generalized linear models and mixed effects models.

While survey researchers make great attempts to standardize their questionnaires including the usage of ratings scales in order to collect unbiased data, respondents are still prone to introducing their own interpretation and bias to their responses. This bias can potentially affect the understanding of commonly investigated drivers of customer satisfaction and limit the quality of the recommendations made to management. One such problem is scale use heterogeneity, in which respondents do not employ a panoramic view of the entire scale range as provided, but instead focus on parts of the scale in giving their responses. Studies have found that bias arising from this phenomenon was especially prevalent in multinational research, e.g., respondents of some cultures being inclined to use only the neutral points of the scale. Moreover, personal variability in response tendencies further complicates the issue for researchers. This paper describes an implementation that uses a Bayesian hierarchical model to capture the distribution of heterogeneity while incorporating the information present in the data. More specifically, SAS^® PROC MCMC is used to carry out a comprehensive modeling strategy of ratings data that account for individual level scale usage. Key takeaways include an assessment of differences between key driver analyses that ignore this phenomenon versus the one that results from our implementation. Managerial implications are also emphasized in light of the prevalent use of more simplistic approaches.

For over three decades, SAS^® has provided capabilities for beating your data into submission. In June of 2000, SAS acquired a company called DataFlux in order to add data quality capabilities to its portfolio. Recently, SAS folded DataFlux into the mother ship. With SAS^® 9.4, SAS^® Enterprise Data Integration Server and baby brother SAS^® Data Integration Server were upgraded into a series of new bundles that still include the former DataFlux products, but those products have grown. These new bundles include data management, data governance, data quality, and master data management, and come in advanced and standard packaging. This paper explores these offerings and helps you understand what this means to both new and existing customers of SAS^® Data Management and DataFlux products. We break down the marketing jargon and give you real-world scenarios of what customers are using today (prior to SAS 9.4) and walk you through what that might look like in the SAS 9.4 world. Each scenario includes the software that is required, descriptions of what each of the components do (features and functions), as well as the likely architectures that you might want to consider. Finally, for existing SAS Enterprise Data Integration Server and SAS^® Data Integration Server customers, we discuss implications for migrating to SAS Data Management and detail some of the functionality that may be new to your organization.

Over the past decade, sports analytics has seen an explosion in research and model development to calculate wins, reaching cult popularity with the release of the film 'Moneyball.' The purpose of this paper is to explore the methodology of solving a real-life Moneyball problem in basketball. An optimal basketball lineup will be selected in an attempt to maximize the total points per game while maximizing court coverage. We will briefly review some of the literature that has explored this type of problem, traditionally called the maximum coverage problem (MCP) in operations research. An exploratory data analysis will be performed, including visualizations and clustering in order to prep the modeling dataset for optimization. Finally, SAS^® will be used to formulate an MCP problem, and additional constraints will be added to run different business scenarios.

More organizations are understanding the importance of geo-tagged data and the need for tools that can successfully combine location data with business metrics to provide intelligent outputs that are beyond a simple map. SAS^® Visual Analytics provides a robust and powerful platform for achieving location intelligence performed with a combination of SAS^® Analytics and GIS mapping technologies such as that offered by Esri. This paper describes the essentials for achieving location intelligence and demonstrates with industry examples how SAS Visual Analytics makes it possible.

This paper considers the %MRE macro for estimating multivariate ratio estimates. Also, we use PROC REG to estimate multivariate regression estimates and to show that regression estimates are superior to the ratio estimates.

Two examples of Vector Autoregressive Moving Average modeling with exogenous variables are given in this presentation. Data is from the real world. One example is about a two-dimensional time series for wages and prices in Denmark that spans more than a hundred years. The other is about the market for agricultural products, especially eggs! These examples give a general overview of the many possibilities offered by PROC VARMAX, such as handling of seasonality, causality testing and Bayesian modeling, and so on.

This presentation features implementation leads from SAS^® Professional Services and Health Canada's Non-Insured Health benefits (NIHB) program, on a joint implementation of SAS^® Fraud Framework for Health Care. The presentation walks through the fast-paced implementation of NIHB's Pharmacy Surveillance System that guards Canadian taxpayers from undue costs, and protects the safety of NIHB clients. This presentation is a blend of project management and technical material, and presents both the client (NIHB) and consultant (SAS) perspectives throughout the story. The presentation converges onto several core principles needed to successfully deliver analytical solutions.

Sometimes the notes, warnings, and errors in the SAS^® Log window can be cryptic, at best. Hours of programming and deciphering the log can make a person feel a little down and somewhat nutty. What if there was a way to make the SAS log informative and amusing at the same time? Having the option to change how the SAS log communicates might actually keep a user from throwing his or her computer out the window. Our aim is to help thousands of SAS programmers understand how the messages in the log can be interpreted in an entertaining way.

Greater data availability leads to potentially greater depth and subtlety of modeling, but building a model and gaining actionable business insight from analytic data is fundamentally a fixed process (there are no short cuts). There are different impacts, however. Big Data analytic processing taxes the process in one way, while analytic exploration taxes it in another.

SAS/OR^® software for operations research includes mathematical optimization, discrete-event simulation, and project and resource scheduling capabilities. This paper surveys a number of its new features that better equip you to address decision-making challenges such as planning, resource management, and asset allocation. Optimization performance improvements help you solve larger, more detailed problems more quickly. Improvements encompass linear, mixed integer linear, and nonlinear optimization, and include multithreading of the mixed integer linear solver and major improvements in the performance and functionality of the decomposition algorithm for linear and mixed integer linear optimization. The OPTMODEL procedure for optimization modeling adds direct access to the same set of efficient network optimization algorithms available via the OPTNET procedure in SAS/OR, enabling you to embed network optimization as a component of larger solution processes. Other new features enable you to execute multiple optimizations in parallel and use the FCMP procedure to define functions. The OPTLSO procedure for global and local search optimization adds the ability to work with multiple objective functions and produce a set of Pareto-optimal solutions. This approach enables you to manage the trade-offs that arise between competing objectives and adds to the range of optimization problems that you can solve using PROC OPTLSO. Another new feature is support for the READ_ARRAY function in PROC FCMP, with which you can much more easily input array-structured data to be used in function definitions. Finally, SAS^® Simulation Studio for discrete-event simulation enhances its graphical interface to better support customization and increase ease of use.

Most programmers are familiar with the directive Know your data. But not everyone knows about all the data and metadata that a SAS^® data set holds or understands what to do with this information. This presentation talks about the majority of these attributes, how to obtain them, why they are important, and what you can do with them. For example, data sets that have been around for a while might have an inordinate number of deleted observations that you are carrying around unnecessarily. Or you might be able to quickly check to determine whether the data set is indexed and if so, by what variables in order to increase your program s performance. Also, engine-dependent data such as owner name and file size is found in PROC CONTENTS output, which is useful for understanding and managing your data. You can also use ODS output in order to use the values of these many attributes programmatically. This presentation shows you how.

In business environments, a common obstacle to effective data-informed decision making occurs when key stakeholders are reluctant to embrace statistically derived predicted values or forecasts. If concerns regarding model inputs, underlying assumptions, and limitations are not addressed, decision makers might choose to trust their gut and reject the insight offered by a statistical model. This presentation explores methods for converting potential critics into partners by proactively involving them in the modeling process and by incorporating simple inputs derived from expert judgment, focus groups, market research, or other directional qualitative sources. Techniques include biasing historical data, what-if scenario testing, and Monte Carlo simulations.

It is often the case that parameters in a predictive model should be restricted to an interval that is either reasonable or necessary given the model s application. A simple and classic example of such a restriction is the regression model which requires that all parameters to be positive. In the case of multiple least squares (MLS) regression, the resulting model is therefore strictly additive and, in certain applications, not only appropriate but also intuitive. This special case of an MLS model is commonly referred to as a nonnegative least squares regression. While Base SAS^® contains a multitude of ways to perform a multiple least squares regression (PROC REG and PROC GLM, to name two), there exists no native SAS^® procedure to conduct a nonnegative least squares regression. The author offers a concise way to conduct the nonnegative least squares analysis by using PRON NLIN (proc non-linear ). PROC NLIN offers user restriction on parameter estimates. By fashioning a linear model in the framework of a nonlinear procedure, the end result can be achieved. As an additional corollary, the author will show how to calculate the _RSQUARE_ statistic for the resulting model, which has been left out of the PROC NLIN output for the reason that it is invalid in most cases (though not ours).

This poster shows the audience step-by-step how to connect to a database without registering the connection in either the Windows ODBC Administrator tool or in the Windows Registry database. This poster also shows how the connection can be more flexible and better managed by building it into a SAS^® macro.

When creating an OLAP cube, you have the option of specifying a drill-through table, also known as a Show Details table. This quick tip discusses the implications of using your detail table as your drill-through table and explores some viable alternatives.

When viewing and working with SAS^® data sets especially wide ones it s often instinctive to rearrange the variables (columns) into some intuitive order. The RETAIN statement is one of the most commonly cited methods used for ordering variables. Though RETAIN can perform this task, its use as an ordering clause can cause a host of easily missed problems due to its intended function of retaining values across DATA step iterations. This risk is especially great for the more novice SAS programmer. Instead, two equally effective and less risky ways to order data set variables are recommended, namely, the FORMAT and SQL SELECT statements.

Part of being a good analyst and statistician is being able to understand the output of a statistical test in SAS^®. P-values are ubiquitous in statistical output as well as medical literature and can be the deciding factor in whether a paper gets published. This shows a somewhat dictatorial side of them. But do we really know what they mean? In a democratic process, people vote for another person to represent them, their values, and their opinions. In this sense, the sample of research subjects, their characteristics, and their experience, are combined and represented to a certain degree by the p-value. This paper discusses misconceptions about and misinterpretations of the p-value, as well as how things can go awry in calculating a p-value. Alternatives to p-values are described, with advantages and disadvantages of each. Finally, some thoughts about p-value interpretation are given. To disarm the dictator, we need to understand what the democratic p-value can tell us about what it represents&.and what it doesn't. This presentation is aimed at beginning to intermediate SAS statisticians and analysts working with SAS/STAT^®.

Have you ever asked, Why doesn't my PDF output look just like my HTML output? This paper explains the power and differences of each destination. You ll learn how each destination works and understand why the output looks the way it does. Learn tips and tricks for how to modify your SAS^® code to make each destination look more like the other. The tips span from beginner to advanced in all areas of reporting. Each destination is like a superhero, helping you transform your reports to meet all your needs. Learn how to use each ODS destination to the fullest extent of its powers.
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PD_Calibrate is a macro that standardizes the calibration of our predictive credit-scoring models at Nykredit. The macro is activated with an input data set, variables, anchor point, specification of method, number of buckets, kink-value, and so on. The output consists of graphs, HTML, and two data sets containing key values for the model being calibrated and values for the use of graphics.

ODS is a power tool for generating HTML-based reports. Quite often, however, there are exacting requirements for report content, layout, and placement that can be done with HTML (and especially HTML5) that can t be done with ODS. This presentation shows several examples that use PROC STREAM and SAS^® Server Pages in a batch (for example, scheduled tasks, using SAS^® Display Manager, using SAS^® Enterprise Guide^®) to generate such custom reports. And yes, despite the name SAS Server Pages, this technology, including the use of jQuery widgets, does apply to batch environments. This paper describes and shows several examples that are similar to those presented in the SAS^® Press book SAS Server Pages: Generating Dynamic Content (http://support.sas.com/publishing/authors/extras/64993b.html) and on the author s blog Jurassic SAS in the BI/EBI World (http://hcsbi.blogspot.com/): creating a custom calendar; a sample mail-merge application; generating a custom Microsoft Excel-based report; and generating an expanding drill-down table.

Quite often when building web applications that use either the SAS^® Stored Process Server or the SAS/IntrNet^® Applications Dispatcher, it is necessary to create a custom user interface to prompt for the needed parameters. For example, generating a custom user interface can be accomplished by chaining stored processes together. The first stored process generates the user interface where the user selects the desired options and uses PROC STREAM to process and input SAS^® Server Pages to display the user interface. The second (or later) stored process in the chain generates the desired output. This paper describes and shows several examples similar to those presented in the SAS^® Press book SAS Server Pages: Generating Dynamic Content (http://support.sas.com/publishing/authors/extras/64993b.html) and on the author s blog Jurassic SAS in the BI/EBI World (http://hcsbi.blogspot.com/).

PROC TABULATE is a powerful tool for creating tabular summary reports. Its advantages, over PROC REPORT, are that it requires less code, allows for more convenient table construction, and uses syntax that makes it easier to modify a table s structure. However, its inability to compute the sum, difference, product, and ratio of column sums has hindered its use in many circumstances. This paper illustrates and discusses some creative approaches and methods for overcoming these limitations, enabling users to produce needed reports and still enjoy the simplicity and convenience of PROC TABULATE. These methods and skills can have prominent applications in a variety of business intelligence and analytics fields.

A time-consuming part of statistical analysis is building an analytic data set for statistical procedures. Whether it is recoding input values, transforming variables, or combining data from multiple data sources, the work to create an analytic data set can take time. The DS2 programming language in SAS^® 9.4 simplifies and speeds data preparation with user-defined methods, storing methods and attributes in shareable packages, and threaded execution on multi-core SMP and MPP machines. Come see how DS2 makes your job easier.

The linear logistic test model (LLTM) that incorporates the cognitive task characteristics into the Rasch model has been widely used for various purposes in educational contexts. However, the LLTM model assumes that the variance of item difficulties is completely accounted for by cognitive attributes. To overcome the disadvantages of the LLTM, Janssen and colleagues (2004) proposed the crossed random-effects (CRE) LLTM by adding the error term on item difficulty. This study examines the accuracy and precision of the CRE-LLTM in terms of parameter estimation for cognitive attributes. The effect of different factors (for example, sample size, population distributions, sparse or dense matrices, and test length), is examined. PROC GLIMMIX was used to do the analysis and SAS/IML^® software was used to generate data.

Graphs in oncology studies are essential for getting more insight about the clinical data. This presentation demonstrates how ODS Graphics can be effectively and easily used to create graphs used in oncology studies. We discuss some examples and illustrate how to create plots like drug concentration versus time plots, waterfall charts, comparative survival plots, and other graphs using Graph Template Language and ODS Graphics procedures. These can be easily incorporated into a clinical report.

The effectiveness of visual interpretation of the differences between pairs of LS-means in a generalized linear model includes the graph's ability to display four inferential and two perceptual tasks. Among the types of graphs which display some or all of these tasks are the forest plot, the mean-mean scatter plot (diffogram), and closely related to it, the mean-mean multiple comparison (MMC) plot. These graphs provide essential visual perspectives for interpretation of the differences among pairs of LS-means from a generalized linear model (GLM). The diffogram is a graphical option now available through ODS statistical graphics with linear model procedures such as GLIMMIX. Through combining ODS output files of the LS-means and their differences, the SGPLOT procedure can efficiently produce forest and MMC plots.

Power analysis helps you plan a study that has a controlled probability of detecting a meaningful effect, giving you conclusive results with maximum efficiency. SAS/STAT^® provides two procedures for performing sample size and power computations: the POWER procedure provides analyses for a wide variety of different statistical tests, and the GLMPOWER procedure focuses on power analysis for general linear models. In SAS/STAT 13.1, the GLMPOWER procedure has been updated to enable power analysis for multivariate linear models and repeated measures studies. Much of the syntax is similar to the syntax of the GLM procedure, including both the new MANOVA and REPEATED statements and the existing MODEL and CONTRAST statements. In addition, PROC GLMPOWER offers flexible yet parsimonious options for specifying the covariance. One such option is the two-parameter linear exponent autoregressive (LEAR) correlation structure, which includes other common structures such as AR(1), compound symmetry, and first-order moving average as special cases. This paper reviews the new repeated measures features of PROC GLMPOWER, demonstrates their use in several examples, and discusses the pros and cons of the MANOVA and repeated measures approaches.

The SQL procedure contains many powerful and elegant language features for intermediate and advanced SQL users. This presentation discusses topics that will help SAS^® users unlock the many powerful features, options, and other gems found in the SQL universe. Topics include CASE logic; a sampling of summary (statistical) functions; dictionary tables; PROC SQL and the SAS macro language interface; joins and join algorithms; PROC SQL statement options _METHOD, MAGIC=101, MAGIC=102, and MAGIC=103; and key performance (optimization) issues.

The use of predictive models in healthcare has steadily increased over the decades. Statistical models now are assumed to be a necessary component in population health management. This session will review practical considerations in the choice of models to develop, criteria for assessing the utility of the models for production, and challenges with incorporating the models into business process flows. Specific examples of models will be provided based upon work by the Health Economics team at Blue Cross Blue Shield of North Carolina.

Over the years, there has been a growing concern about consumption of tobacco among youth. But no concrete studies have been done to find what exactly leads the children to start consuming tobacco. This study is an attempt to figure out the potential reasons for the same. Through our analysis, we have also tried to build A model to predict whether a child would smoke next year or not. This study is based on the 2011 National Youth Tobacco Survey data of 18,867 observations. In order to prepare data for insightful analysis, imputation operations were performed on the data using tree-based imputation methods. From a pool of 197 variables, 48 key variables were selected using variable selection methods, partial least squares, and decision tree models. Logistic Regression and Decision Tree models were built to predict whether a child would smoke in the next year or not. Comparing the models using Misclassification rate as the selection criteria, we found that the Stepwise Logistic Regression Model outperformed other models with a Validation Misclassification of 0.028497, 47.19% Sensitivity and 95.80% Specificity. Factors such as company of friends, cigarette brand ads, accessibility to the tobacco products, and passive smoking turned out to be the most important predictors in determining a child smoker. After this study, we could outline some important findings like the odds of a child taking up smoking are 2.17 times high when his close friends are also smoking.

An increase in sea levels is a potential problem that is affecting the human race and marine ecosystem. Many models are being developed to find out the factors that are responsible for it. In this research, the Memory-Based Reasoning model looks more effective than most other models. This is because this model takes the previous solutions and predicts the solutions for forthcoming cases. The data was collected from NASA. The data contains 1,072 observations and 10 variables such as emissions of carbon dioxide, temperature, and other contributing factors like electric power consumption, total number of industries established, and so on. Results of Memory-Based Reasoning models like RD tree, scan tree, neural networks, decision tree, and logistic regression are compared. Fit statistics, such as misclassification rate and average squared error are used to evaluate the model performance. This analysis is used to predict the rise in sea levels in the near future and to take the necessary actions to protect the environment from global warming and natural disasters.

Introduction to the PAR Framework, a non-profit, member-driven collaborative for student success providing affordable predictive analytics, innovative benchmark reports, and intervention assessment tools to colleges and universities nationwide.

The steady expansion of electronic health records (EHR) over the past decade has increased the use of observational healthcare data for analysis. One of the challenges with EHR data is to combine information from different domains (diagnosis, procedures, drugs, adverse events, labs, quality of life scores, and so on) onto a single timeline to get a longitudinal view of the patient. This enables the physician or researcher to visualize a patient's health profile, thereby revealing anomalies, trends, and responses graphically,thus empowering them to treat more effectively. This paper attempts to provide a composite view of a patient by using SAS^® Graph Template Language to create a profile graph using the following data elements: key event dates, drugs, adverse events, Quality of Life (QoL) scores. For visualization, the GTL graph uses X and X2 axes for dates, vertical reference lines to represent key dates (for example, when the disease is first diagnosed), horizontal bar plot for duration of drugs taken and adverse events reported, and a series plot at the bottom to show the QoL score.

Sparse data sets are common in applications of text and data mining, social network analysis, and recommendation systems. In SAS^® software, sparse data sets are usually stored in the coordinate list (COO) transactional format. Two major drawbacks are associated with this sparse data representation: First, most SAS procedures are designed to handle dense data and cannot consume data that are stored transactionally. In that case, the options for analysis are significantly limited. Second, a sparse data set in transactional format is hard to store and process in distributed systems. Most techniques require that all transactions for a particular object be kept together; this assumption is violated when the transactions of that object are distributed to different nodes of the grid. This paper presents some different ideas about how to package all transactions of an object into a single row. Approaches include storing the sparse matrix densely, doing variable selection, doing variable extraction, and compressing the transactions into a few text variables by using Base64 encoding. These simple but effective techniques enable you to store and process your sparse data in better ways. This paper demonstrates how to use SAS^® Text Miner procedures to process sparse data sets and generate output data sets that are easy to store and can be readily processed by traditional SAS modeling procedures. The output of the system can be safely stored and distributed in any grid environment.

In this session, you learn how Kaiser Permanente has taken a centralized production support approach to using SAS^® Enterprise Guide^® 4.3 in the healthcare industry. Kaiser Permanente Northwest (KPNW) has designed standardized processes and procedures that have allowed KPNW to streamline the support of production content, which enabled KPNW analytical resources to focus more on new content development rather than on maintenance and support of steady state programs and processes. We started with over 200 individual SAS^® processes across four different SAS platforms, SAS Enterprise Guide, Mainframe SAS^®, PC SAS^® and SAS^® Data Integration Studio, in oder to standardize our development approach on SAS Enterprise Guide and build efficient and scalable processes within our department and across the region. We walk through the need for change, how the team was set up, provide an overview of the UNIX SAS platform, walk through the standard production requirements (developer pack), and review lessons learned.

In a good clinical study, statisticians and various stakeholders are interested in assessing and isolating the effect of non-study drugs. One common practice in clinical trials is that clinical investigators follow the protocol to taper certain concomitant medications in an attempt to prevent or resolve adverse reactions and/or to minimize the number of subject withdrawals due to lack of efficacy or adverse event. To assess the impact of those tapering medicines during study is of high interest to clinical scientists and the study statistician. This paper presents the challenges and caveats of assessing the impact of tapering a certain type of concomitant medications using SAS^® 9.3 based on a hypothetical case. The paper also presents the advantages of visual graphs in facilitating communications between clinical scientists and the study statistician.

Many SAS^® procedures use classification variables when they are processing the data. These variables control how the procedure forms groupings, summarizations, and analysis elements. For statistics procedures, they are often used in the formation of the statistical model that is being analyzed. Classification variables can be explicitly specified with a CLASS statement, or they can be specified implicitly from their usage in the procedure. Because classification variables have such a heavy influence on the outcome of so many procedures, it is essential that the analyst have a good understanding of how classification variables are applied. Certainly there are a number of options (system and procedural) that affect how classification variables behave. While you may be aware of some of these options, a great many are new, and some of these new options and techniques are especially powerful. You really need to be open to learning how to program with CLASS.

Multi-site health science-related, distributed data networks are becoming increasingly popular, particularly at a time where big data and privacy are often competing priorities. Distributed data networks allow individual-level data to remain behind the firewall of the data holder, permitting the secure execution of queries against those local data and the return of aggregated data produced from those queries to the requester. These networks allow the use of multiple, varied sources of data for study purposes ranging from public health surveillance to comparative effectiveness research, without compromising data holders concerns surrounding data security, patient privacy, or proprietary interests. This paper focuses on the experiences of the Mini-Sentinel pilot project as a case study for using SAS^® to design and build infrastructure for a successful multi-site, collaborative, distributed data network. Mini-Sentinel is a pilot project sponsored by the U.S. Food and Drug Administration (FDA) to create an active surveillance system the Sentinel System to monitor the safety of FDA-regulated medical products. The paper focuses on the data and programming aspects of distributed data networks but also visits governance and administrative issues as they relate to the maintenance of a technical network.

Do you find it difficult to dress up your graphs for your reports or presentations? SAS^® 9.4 introduced new capabilities in ODS Graphics that give you the ability to style your graphs without creating or modifying ODS styles. Some of the new capabilities include the following: a new option for controling how ODS styles are applied graph syntax for overriding ODS style attributes for grouped plots the ability to define font glyphs and images as plot markers enhanced attribute map support In this presentation, we discuss these new features in detail, showing examples in the context of Graph Template Language and ODS Graphics procedures.

Can you juggle? Maybe. Can you shuffle a deck of cards? Probably. Can you do both at the same time? Welcome to the world of SAS^® and LSF! Very few SAS Administrators start out learning LSF at the same time they learn SAS; most already know SAS, possibly starting out as a programmer or analyst, but now have to step up to an enterprise platform with shared resources. The biggest challenge on an enterprise platform? How to share! How to maximum the utilization of a SAS platform, yet still ensure everyone gets their fair share? This presentation will boil down the 2000+ pages of LSF documentation to provide an introduction into various LSF concepts: * Host * Clusters * Nodes * Queues * First-Come-First-Serve * Fairshare * and various configuration settings: UJOB_LIMIT, PJOB_LIMIT, etc. Plus some insight on where to configure all these settings which are set up by the installation process, and which can be configured by the SAS or LSF administrator. This session is definitely NOT for experts. It is for those about to step into an enterprise deployment of SAS, and want to understand how the SAS server sessions they know so well can run on a shared platform.

Are you time-poor and code-heavy? It's easy to get into a rut with your SAS^® code, and it can be time-consuming to spend your time learning and implementing improved techniques. This presentation is designed to share quick improvements that take five minutes to learn and about the same time to implement. The quick hits are applicable across versions of SAS and require only Base SAS^® knowledge. Included topics are: simple macro tricks little-known functions that get rid of messy coding dynamic conditional logic data summarization tips to reduce data and processing testing and space utilization tips. This presentation has proven valuable to beginner through experienced SAS users.

If someone comes to you with hundreds of questionnaire forms in Microsoft Word file format and asks you to extract the data from the forms into a SAS^® data set, you might have several ways to handle this. However, as a SAS programmer, the shortcut is to write a SAS program to read the data directly from Word files into a SAS data set. This paper shows how it can be done with simple SAS programming skills, such as using FILENAME with the PIPE option, DDE, function call EXECUTE( ), and so on.

The ZIP access method is new with SAS^® 9.4. This paper provides several examples of reading from and writing to ZIP files using this access method, including the use of the DATA step directory management macros and the new MEMVAR= option.

The Department of Market Monitoring (DMM) at California ISO is responsible for promoting a robust, competitive, and nondiscriminatory electric power market in California by keeping a close watch on the efficiency and effectiveness of the ancillary service, congestion management, and real-time spot markets. We monitor the potential of market participants to exercise undue market power, the behavior of market participants that is consistent with attempts to exercise market power and the market performance that results from the interaction of market structure with participant behavior. In order to perform monitoring activities effectively, DMM collects available data, designs, and implement reporting dashboards that track key market metrics. We are using various SAS^® BI tools to develop and employ metrics and analytic tools applicable to market structure, participant behavior, and market performance. This paper provides details about the effective use of various SAS BI tools to implement an automated real time market monitoring functionality.

With the coming of very big data sets, timely analysis and fast recommendations of items for industries to users are of particular importance. The SAS^® LASR analytic server, which processes requests at great speed due to its high-performance, multi-threaded and gridded analytic code, provides an in-memory analytic platform for our recommendation system and makes it possible for rapid and accurate recommendations. This paper describes how PROC recommend in SAS^® LASR analytic server works from loading tables, selecting models, tuning parameters to final recommending. The paper also uses movie rating data sets to show how to evaluate different models based on various metrics.

Predicting news articles that customers are likely to view/read next provides a distinct advantage to news sites. Collaborative filtering is a widely used technique for the same. This paper details an approach within collaborative filtering that uses the cosine similarity function to achieve this purpose. The paper further details two different approaches, customized targeting and article level targeting, that can be used in marketing campaigns. Please note that this presentation connects with Session ID 1887. Session ID 1887 happens immediately following this session

Personalized recommender systems are being used in many industries to increase customer engagement. In the TV industry, this is primarily used to increase viewership, which in turn increases market share, revenue, and profit. This paper attempts to develop a recommender system using the correlation procedure under collaborative filtering methodology. The only data requirement for this recommendation system would be past viewership of customers for a given time period. Please note that this session connects with Session ID 1886. Session ID 1886 happens immediately prior to this session

Healthcare expenditure growth continues to be a prominent healthcare policy issue, and the uncertain impact of the Affordable Care Act (ACA) has put increased pressure on payers to find ways to exercise control over costs. Fueled by provider performance analytics, BCBSNC has developed innovative strategies that recognize, reward, and assist providers delivering high-quality and efficient care. A leading strategy has been the introduction of a new tiered network product called Blue Select, which was launched in 2013 and will be featured in the State Health Exchange. Blue Select is a PPO with differential member cost-sharing for tiered providers. Tier status of providers is determined by comparing providers to their peers on the efficiency and quality of the care they delivery. Providers who meet or exceed the standard for quality, measured using Healthcare Effectiveness Data and Information Set (HEDIS) adherence rates and potentially avoidable complication rates for certain procedures, are then evaluated on their case-mix adjusted costs for total episodic costs. Each practice s performance is compared, through indirect standardization, to expected performance, given the patients and conditions treated within a practice. A ratio of observed to expected performance is calculated for both cost and quality to use in determining the tier status of providers for Blue Select. While the primary goal of provider tiering is cost containment through member steerage, the initiative has also resulted in new and strengthened collaborative relationships between BCBSNC and providers. The strategy offers the opportunity to bend the cost curve and provide meaningful change in the quality of healthcare delivery.

Analyzing automobile policies and claims is an ongoing area of interest to the insurance industry. Although there have been many data mining projects in insurance sector over the past decade, the following questions How can insurance firms retain their best customers? Will this damaged car be covered and get claim payment? How much of loss of claims associated with this policy will be? do remain as common. This study applies data mining techniques using SAS^® Enterprise Miner^™ to enhance insurance policies and claims. The main focus is on assessing how corporate fleet customers policy characteristics and claim behavior are different from that of non-fleet customers. With more than 100,000 data records, implementing advanced analytics help create better planning for policy and claim management strategy.

Duration and severity data arise in several fields including biostatistics, demography, economics, engineering, and sociology. SAS^® procedures LIFETEST, LIFEREG. and PHREG are the workhorses for analysis of time to event data in applications in biostatistics. Similar methods apply to the magnitude or severity of a random event, where the outcome might be right, left, or interval censored and/or, right or left truncated. All combinations of types of censoring and truncation could be present in the data set. Regression models such as the accelerated failure time model, the Cox model, and the non-homogeneous Poisson model have extensions to address time-varying covariates in the analysis of clustered outcomes, multivariate outcomes of mixed types, and recurrent events. We present an overview of new capabilities that are available in the procedures QLIM, QUANTLIFE, RELIABILITY, and SEVERITY with examples illustrating their application using empirical data sets drawn from easily accessible sources.

In healthcare, we often express our analytics results as being adjusted . For example, you might have read a study in which the authors reported the data as age-adjusted or risk-adjusted. The concept of adjustment is widely used in program evaluation, comparing quality indicators across providers and systems, forecasting incidence rates, and in cost-effectiveness research. In order to make reasonable comparisons across time, place, or population, we need to account for small sample sizes and case-mix variation in other words, we need to level the playing field and account for differences in health status and for uniqueness in a given population. If you are new to healthcare. What it really means to adjust the data in order to make comparisons might not be obvious. In this paper, we explore the methods by which we control for potentially confounding variables in our data. We do so through a series of examples from the healthcare literature in both primary care and health insurance. In this survey of methods, we discuss the concepts of rates and how they can be adjusted for demographic strata (such as age, gender, and race), as well as health risk factors such as case mix.

This presentation takes a look at DirectPay, a company that collects and buys consumer claims of all types. It developed a model with SAS^® Enterprise Miner^™ to determine the risk of fraud by a debtor and a debtor's creditworthiness. This model is focused on the added value of more and better data. Since 2010, all credit and fraud scores have been calculated using DirectPay's own data and models. In addition, the presentation explores the use of SAS^® Visual Analytics as both a management information and an analytical tool since early 2013.

Over the last decade, five companies have begun to aggressively reshape the landscape of multiple industries and to change retailing forever. They are the Tech Titans: Amazon, Apple, eBay, Facebook, and Google. At last year s SAS^® Retail Users Group, we spoke of these titans. They ve made such an impact since then that they deserve revisiting. Several other tech giants who also have this same potential are joining the battle. These companies are taking a formidable bite not only out of retailing, but also out of advertising, publishing, movies, television, communications, financial services, health care, and insurance. This session highlights the strategies of these companies and what progressive retailers are doing to not only fight back, but to leverage the titans.

This paper reveals the human mobility behavior in the metropolitan area of Rio de Janeiro, Brazil. The base for this study is the mobile phone data provided by one of the largest mobile carriers in Brazil. Mobile phone data comprises a reasonable variety of information, including data about time and location for call activity throughout urban areas. This information might be used to build users trajectories over time, describing the major characteristics of the urban mobility within the city. A variety of distribution analyses is presented in this paper aiming clearly describes the most relevant characteristics of the overall mobility in the metropolitan area of Rio de Janeiro. In addition to that, methods from physics to describe trends in trips such as gravity and radiation models were computed and compared in terms of granularity of the geographic scales and also in relation to traditional data mining approach such as linear regressions. A brief comparison in terms of performance in predicting the amount of trips between pairs of locations is presented at the end.

The project focuses on using analytics to reveal unwarranted use of access to medical records, i.e. employees in health organizations that access information about neighbours, friends, celebrities, etc., without a sound reason to do so. The method is based on the natural assumption that the vast majority of lookups are legitimate lookups that differ from a statistically defined normal behavior will be subject to manual investigation. The work was carried out in collaboration between SAS Institute Norway and the largest Norwegian hospital, Oslo University Hospital (OUS) and was aimed at establishing whether the method is suitable for unveiling unwarranted lookups in medical records. A number of so called scenarios are used to indicate adverse behaviour, each responsible for looking at one particular aspect of journal access data. For instance, one scenario determines the timeliness of a lookup relative to the patient's admission history; another judges whether the medical competency of the employee is relevant to the situation of the patient at the time of the lookup. We have so far designed and developed a library of around 20 scenarios that together are used in weighted combination to render a final judgment of the appropriateness of the lookup. The approach has been proven highly successful, and a further development of these ideas is currently being done, the aim of which is to establish a joint Norwegian solution to the problem of unwarranted access. Furthermore, we believe that the approach and the framework may be utilised in many other industries where sensitive data is being processed, such as financial, police, tax and social services. In this paper, the method is outlined, as well as results of its application on data from OUS.

Spinal epidural abscess (SEA) is a serious complication in hemodialysis (HD) patients, yet there is little medical literature that discusses it. This analysis identified risk factors and co-morbidities associated with SEA, as well as risk factors for mortality following the diagnosis. All incident HD cases from the United States Renal Data System for calendar years 2005 2008 were queried for a diagnosis of SEA. Potential clinical covariates, survival, and risk factors were recovered using ICD-9 diagnosis codes. Log-binomial regressions were performed using PROC GENMOD to assess the relative risks, and Cox regression models were run using PROC PHREG to estimate hazard ratios for mortality. For the 4-year study period, 660/355084 (0.19%) HD patients were identified with SEA, the largest cohort to date. Older age (RR=1.625), infectious comorbidities including bacteremia (RR=7.7976), methicillin-resistant Staphylococcus aureus infection (RR=2.6507), hepatitis C (RR=1.545), and non-infectious factors including diabetes (RR=1.514) and presence of vascular catheters (RR=1.348) were identified as significant risk factors for SEA. SEA in HD patients was associated with an increased risk of death (HR=1.20). Older age (HR=2.269), the presence of dialysis catheters (HR=1.884), cirrhosis (HR=1.715), decubitus ulcers (HR=1.669), bacteremia (HR=1.407), and total parenteral nutrition (HR=1.376) constitute the greatest risk factors for death after SEA diagnosis and thus necessitate a comprehensive approach to management.

Guidelines from the International Conference on Harmonisation (ICH) suggest that clinical trial data should be actively monitored to ensure data quality. Traditional interpretation of this guidance has often led to 100 percent source data verification (SDV) of respective case report forms through on-site monitoring. Such monitoring activities can also identify deficiencies in site training and uncover fraudulent behavior. However, such extensive on-site review is time-consuming, expensive and, as is true for any manual effort, limited in scope and prone to error. In contrast, risk-based monitoring makes use of central computerized review of clinical trial data and site metrics to determine whether sites should receive more extensive quality review through on-site monitoring visits. We demonstrate a risk-based monitoring solution within JMP^® Clinical to assess clinical trial data quality. Further, we describe a suite of tools used for identifying potentially fraudulent data at clinical sites. Data from a clinical trial of patients who experienced an aneurysmal subarachnoid hemorrhage provide illustration.

The growth in the use of Hadoop is changing the way organizations are managing data for analytics. More and more data is being captured and stored in Hadoop -- with the intention of feeding analytics. The way data must be structured for anlaytics hasn't changed. But, because of the volume, there is a clear need for new tools and options for managing data and analytic base tables (ABTs). Hear what SAS sees in the Hadoop arena and how we are addressing this space.

Decision trees and random bootstrap forests are among the most popular algorithms for data mining applications and competitions. Decision trees are easy to interpret and bootstrap forests are very competitive automated classifiers. SAS In-memory Statistics for Hadoop is a new product that provides a decision tree fit action based on the C4.5 algorithm, and ensemble of decision trees action based on bootstrap sampling. This paper introduces how to use SAS In-memory Statistics for Hadoop to apply random bootstrap forests for feature selection, clustering of unlabeled data, and outlier detections.

Ushering in the age of Agile Analytics, SAS Solutions OnDemand substantiates Cloud Focused architectures through both Software-as-a-Service (SaaS) and Enterprise Hosting of SAS Solutions. Supporting almost 500 customer sites , tens of thousands of users, across 70 countries, has yielded a proven track record of success for deploying solution clouds for our industry leading business analytics. If you are an Enterprise Architect, SAS Architect, DBA, data manager, or responsible for SAS cloud based deployments, come hear about some of our top challenges, innovative techniques and best practices, database performance optimizations and other SAS/ACCESS related efficiencies from a data driven perspective. We ll look at how using new features in Oracle Database 12c such as Multitenancy and In Memory on our Oracle Exadata systems will further increase efficiencies towards lowering TCO while maintaining the highest standards of security,availability, agility and performance that our customers demand andexpect.

SAS9.4 brings a lot of progress to the interoperability between SAS and Hadoop -the industry standard for Big Data. This talk brings you up to date with where we are : - more distributions, more data types, more options :-) You now have the choice to run a stand-alone cluster for SAS HPA and VA, or to co-mingle your SAS processing on your general purpose clusters -we'll detail some of the pros and cons of each approach, and explore how advances in Hadoop like YARN will make managing the shared cluster easier going forwards.

Visionaries Paul Kent, SAS Vice President, Big Data and David Lawler, Oracle Senior Vice President, Product Management and Strategyshare their strategic insight as to how and why companies must leverage the 3rd Platform in order to be successful. IDC defines the 3rd Platform as the convergence of Big Data, Cloud, Mobility and Social Media predicting acceleration of uptake for 2014. This session discusses how SAS High-Performance Analytics solutions are tackling today s big data challenges and requisite union to what IDC refers to as data-optimized cloud platforms . The benefits of the collaborative effort between SAS and Oracle enable joint customers to realize tangible value by analyzing all their data, quickly, safely and with the necessary agility to reduce time to insight. What questions should Data Scientists & IT be asking in their Big Data pursuits? How does the convergence of In-Memory and In-Database create the backbone of these data-optimized cloud platforms? #DontMiss.

Friends are funny things. They can be fierce rivals or work great together as a team -- sometimes both at the same time! It's the same with SAS and SAP. There are cases where SAS and SAP compete, but at the same time, we both recognize that we can also work together. SAS and SAP have a long history of working together as we both evolve our technologies. This paper will provide an overview of how SAS can help derive more value from your SAP deployment.

SAS/STAT^® 13.1 brings valuable new techniques to all sectors of the audience forSAS statistical software. Updates for survival analysis include nonparametricmethods for interval censoring and models for competing risks. Multipleimputation methods are extended with the addition of sensitivity analysis.Bayesian discrete choice models offer a modern approach for consumer research.Path diagrams are a welcome addition to structural equation modeling, and itemresponse models are available for educational assessment. This paper providesoverviews and introductory examples for each of the new focus areas in SAS/STAT13.1. The paper also provides a sneak preview of the follow-up release,SAS/STAT 13.2, which brings additional strategies for missing data analysis andother important updates to statistical customers.

SAS^® 9.4 introduces several new software products to better support SAS^® web applications. These products include SAS^® Web Server, SAS^® Web Application Server (with the availability of out-of-the-box clustering), and SAS^® Environment Manager. Even though these products have been tuned and tested for SAS 9.4 web applications, advanced users might want to know the tools and techniques that they can use to further monitor, manage, tune, and improve the performance of their environment. This paper discusses how customers can achieve that by exploring the following concepts, activities, techniques, and tools: using SAS Environment Manager to monitor run-time performance of middle-tier components using additional tools to monitor middle-tier components (Apache server-status, Java VisualVM, Java command-line tools, Java GC logging) identifying the potential bottlenecks and tuning suggestions identifying appropriate clustering strategy (single-server vs. multi-server for homogenous or heterogeneous clustering) suggesting the data to collect when analyzing performance (GC data, thread dumps, heapdumps, system resource utilization information, log files) discussing in-depth performance analysis tools (Thread Dump Analyzer, HPjmeter, Eclipse Memory Analyzer (MAT), IBM Support Assistant tools: GC and Memory Visualizer, Memory Analyzer, Thread, and Monitor Dump Analyzer)

Why would a SAS^® administrator need a dashboard? With the evolution of SAS^®9, the SAS administrator s role has dramatically changed. Creating a dashboard on a SAS environment gives the SAS administrator an overview on the environment health, ensures resources are used as predicted, and provides a way to explore. SAS^® Visual Analytics allows you to quickly explore, analyze, and visualize data. So, why not bring the two concepts together? In this session, you will learn tips for designing dashboards, loading what might seem like impossible data, and building visualizations that guide users toward the next level of analysis. Using the dashboard, SAS administrators will learn ways to determine the system health and how to take advantage of external tools, such as the Metacoda software, to find additional insights and explore problem areas.

The high school dropout problem has been called a national crisis (Heppen & Therriault, 2008). Almost one-third of all high school students leave the public school system before graduating (Swanson, 2004), and the problem is particularly severe among minority students (Greene & Winters, 2005; U.S. Department of Education, 2006). Educators, researchers, and policymakers continue to work to identify effective dropout prevention strategies. One effective approach is to identify high-risk students at an early stage, and then provide corresponding interventions to keep them in school. One of the strengths of Educational Data Mining is to reveal hidden patterns and predict future performance by analyzing accessible student data. These predictive algorithms generated by predictive modeling can serve as an early warning system. However, because individual schools and districts have various combinations of race, gender, and socioeconomic status, we cannot use a set of standardized predictors and obtain satisfactory predictive results. Analyzing a limited number of variables and limited historical data does not generate accurate models. Additionally, the predictive model might not consider interactions among predictors. The strength of data mining is the capability to analyze a large amount of data and variables. Multiple analytic strategies (including model comparisons) can be applied to maximize model performance. For future goals, we propose a debuted data mining framework to construct an early warning and trend analysis system with components of data warehousing, data mining, and reporting at the levels of individual students, schools, school districts, and the entire state.

SAS^® Enterprise Guide^® has become the place through which many SAS^® users access the power of SAS. Some like it, some loathe it, some have never known anything else. In my experience, the following attitudes prevail regarding the product: 1) I don't know what SAS is, but I can use a mouse and I know what my business needs are. 2) I've used SAS before, but now my company has moved to SAS Enterprise Guide and I love it! 3) I've used SAS before, but now my company has done something really stupid. SAS Enterprise Guide offers a place to learn as well as work. The product offers environments for point-and-click for those who want that, and a type-your-code-with-semi-colons environment for those who want that. Even better, a user can mix and match, using the best of both worlds. I show that SAS Enterprise Guide is a great place for building up business solutions using a step-by-step method, how we can make the best of both environments, and how we can dip our toes into parts of SAS that might have frustrated us in the past and made us run away and cry I ll do it in Excel! I demonstrate that there are some very nice aspects to SAS Enterprise Guide, out of the box, that are often ignored but that can improve the overall SAS experience. We look at my personal nemeses, SAS/GRAPH^® and PROC TABULATE, with a side-trip to the mysterious world that is ODS, or the Output Delivery System.

Have you been programming in SAS^® for a while and just aren t sure how SAS^® Enterprise Guide^® can help you? This presentation demonstrates how SAS programmers can use SAS Enterprise Guide 5.1 as their primary interface to SAS, while maintaining the flexibility of writing their own customized code. We explore: navigating and customizing the SAS Enterprise Guide environment using SAS Enterprise Guide to access existing programs and enhance processing exploiting the enhanced development environment including syntax completion and built-in function help using SAS^® Code Analyzer, Report Builder, and Document Builder adding Project Parameters to generalize the usability of programs and processes leveraging built-in capabilities available in SAS Enterprise Guide to further enhance the information you deliver Our audience is SAS users who understand the basics of SAS programming and want to learn how to use SAS Enterprise Guide. This paper is also appropriate for users of earlier versions of SAS Enterprise Guide who want to try the enhanced features available in SAS Enterprise Guide 5.1.

Changes in default behavior in the last few SAS^® releases have enabled faster processing of SAS formats, especially for SAS/ACCESS^® customers. But, as with any performance enhancement, your results may vary. This presentation teaches you: the differences between two important SAS format optimizations how to tell which optimization is in effect a simple method to get the behavior you want The target audience for this presentation is SAS/ACCESS customers, particularly those who have also licensed SAS^® In-Database Code Accelerator for Teradata or SAS^® In-Database Code Accelerator for Greenplum.

Speed, precision, reliability these are just three of the many challenges that today s banking institutions need to face. Join Austria s ERSTE GROUP Bank on their road from monolithic processing toward a highly flexible processing infrastructure using SAS^® Grid technology. This paper focuses on the central topics and decisions that go beyond the standard material about the product that is presented initially to SAS Grid prospects. Topics covered range from how to choose the correct hardware and critical architecture considerations to the necessary adaptions of existing code and logic all of which have shown to be a common experience for all the members of the SAS Grid community. After making the initial plans and successfully managing the initial hurdles, seeing it all come together makes you realize the endless possibilities for improving your processing landscape.

As organizations deploy SAS^® applications to produce the analytical results that are critical for solid decision making, they are turning to distributed grid computing operated by SAS^® Grid Manager. SAS Grid Manager provides a flexible, centrally managed computing environment for processing large volumes of data for analytical applications. Exceptional storage performance is one of the most critical components of implementing SAS in a distributed grid environment. When the storage subsystem is not designed properly or implemented correctly, SAS applications do not perform well, thereby reducing a key advantage of moving to grid computing. Therefore, a well-architected SAS environment with a high-performance storage environment is integral to clients getting the most out of their investment. This paper introduces concepts from software storage virtualization in the cloud for the generalized SAS Grid Manager architecture, highlights platform and enterprise architecture considerations, and uses the most popularly selected distributed file system, IBM GPFS, as an example. File system scalability considerations, configuration details, and tuning suggestions are provided in a manner that can be applied to a client s own environment. A summary checklist of important factors to consider when architecting and deploying a shared, distributed file system is provided.

There are exciting new capabilities available from SAS^® High-Performance Analytics and SAS^® Visual Analytics. Current customers seek a deployment strategy that enables gradual migration to the new technologies. Such a strategy would mitigate the need for 'rip and replace' and would enable resource utilization to evolve along a continuum rather than partitioning resources, which would result in underused computing or storage hardware. New customers who deploy a combination of SAS^® Grid Manager, SAS High-Performance Analytics, and SAS Visual Analytics seek to reduce the cost of computing resources and reduce data duplication and data movement by deploying these solutions on the same pool of hardware. When sharing hardware, it is important to implement resource management in order to help guarantee that resources are available for critical applications and processes. This session discusses various methods for managing hardware resources in a multi-application environment. Specific strategies are suggested, along with implementation suggestions.

In this hands-on workshop, we introduce the highly interactive IMSTAT procedure for developing a variety of statistical and machine-learning models. We emphasize collocation and management of interactive analytics within a Hadoop cluster. You learn how to prepare and load data from HDFS into a SAS LASR^® Analytic Server session, summarize and explore the data, compute temporary columns, use GROUP BY, develop logistic and OLS regression models, use decision trees and random woods model, evaluate and deploy, and build recommendation models using PROC RECOMMEND.

This discussion uses SAS^® Office Analytics as an example to demonstrate the importance of preparing for the SAS^® installation. There are many nuances as well as requirements that need to be addressed before you do an installation. These requirements are basically similar, yet they differ according to the target installation operating system. In other words, there are some differences in preparation routines for Windows and *Nix flavors. Our discussion focuses on these three topics: 1. Pre-installation considerations such as sizing, storage, proper credentials, and third-party requirements; 2. Installation steps and requirements; and 3. Post-installation configuration. In addition to preparation, this paper also discusses potential issues and pitfalls to watch out for, as well as best practices.

You've been coding in Base SAS^® for a while. You've seen it, maybe even run code written by someone else, but there is something about the SAS^® Macro Language that is preventing you from fully embracing it. Could it be that % sign that appears everywhere, that &, that &&, or even that dreaded &&&? Fear no more. This short presentation will make everything clearer and encourage you to start coding your own SAS macros.

Are you wondering what is causing your valuable machine asset to fail? What could those drivers be, and what is the likelihood of failure? Do you want to be proactive rather than reactive? Answers to these questions have arrived with SAS^® Predictive Asset Maintenance. The solution provides an analytical framework to reduce the amount of unscheduled downtime and optimize maintenance cycles and costs. An all new (R&D-based) version of this offering is now available. Key aspects of this paper include: Discussing key business drivers for and capabilities of SAS Predictive Asset Maintenance. Detailed analysis of the solution, including: Data model Explorations Data selections Path I: analysis workbench maintenance analysis and stability monitoring Path II: analysis workbench JMP^®, SAS^® Enterprise Guide^®, and SAS^® Enterprise Miner^™ Analytical case development using SAS Enterprise Miner, SAS^® Model Manager, and SAS^® Data Integration Studio SAS Predictive Asset Maintenance Portlet for reports A realistic business example in the oil and gas industry is used.

This presentation provides users with an update on retail solution releases that have occurred in the past year and a roadmap for moving forward.

Hospital readmission rates have become a key indicator for measuring the quality of health care. Currently, use of these rates has been adopted by major healthcare stakeholders, including the Centers for Medicare & Medicaid Services (CMS), the Agency for Healthcare Research and Quality (AHRQ), and the National Committee for Quality Assurance (NCQA). In the calculation of the readmission rate, it is often a challenging task to identify eligible hospital readmissions from the convoluted administrative claims data. By taking advantage of the flexibility and power of SAS^® programming tools, this paper proposes three different solutions using both DATA step and PROC SQL to help identify 30-day hospital readmissions more efficiently and accurately. Solution 1 (DATA STEP vertically) employs the LAG function to calculate the gap between the current admission date and the immediate previous discharge date. This vertical thinking process is straightforward and does not require additional data management. Solution 2 (DATA STEP horizontally) uses PROC TRANSPOSE procedures, ARRAYs, and DO loops to transform claims data from long to wide, and examines each patient s hospitalization experiences in just one line. A similar horizontal thinking process has been discussed in previous SAS papers for calculating medication utilization. Solution 3 (PROC SQL) takes advantage of a special table joining (self-join) by creating a Cartesian product further subsetted by a joining condition and WHERE statements. All three solutions have achieved the same results by correctly identifying 30-day hospital readmissions, and they can be handily applied to tackle similar programming challenges in research projects.

The UNIX host group delivers many utilities that go unnoticed. What are these utilities, and what can they tell you about your SAS^® system? Are you having authentication problems? Are you unable to get a result from a workspace server? What hot fixes have you applied? These are subjects that come up during a tech support call. It would be good to have background information about these tools before you have to use them.

Universities in the UK are now subject to League Table reporting by a range of providers. The criteria used by each League Table differ. Universities, their faculties, and individual subject areas want to understand how the different tables are constructed and calculated, and what is required in order to maximize their position in each league table in order to attract the best students to their institution, thereby maximizing recruitment and student-related income streams. The School of Computing and Maths at the University of Derby is developing the use SAS^® Visual Analytics to analyse each league table to provide actionable insights as to actions that can be taken to improve their relative standing in the league tables and also to gain insights into feasible levels of targets relative to the peer groups of institutions. This paper outlines the approaches taken and some of the critical insights developed that will be of value to other higher education institutions in the UK, and suggests useful approaches that might be valuable in other countries.

SAS^® Visual Analytics delivers the power of approachable in-memory analytics in an intuitive web interface. The scalable technology behind SAS Visual Analytics should not benefit just the analyst or data scientist in your organization but indeed everyone regardless of their analytical background. This paper outlines a framework for the creation of a cloud deployment of SAS Visual Analytics using the SAS^® 9.4 platform. Based on proven best practices and existing customer implementations, the paper focuses on architecture, processes, and design for reliability and scalable multi-tenancy. The framework enables your organization to move away from the departmental view of the world and to offer analytical capabilities for consumerization and collaboration across the enterprise.

This workshop provides hands-on experience using tools in the SAS^® Data Management offering. Workshop participants will use the following products: SAS^® Data Integration Studio DataFlux^® Data Management Studio SAS^® Data Management Console

This workshop provides hands-on experience using SAS^® Enterprise Miner^™. Workshop participants will do the following: open a project create and explore a data source build and compare models produce and examine score code that can be used for deployment

This workshop provides hands-on experience using SAS^® Forecast Server. Workshop participants will do the following: create a project with a hierarchy generate multiple forecasts automatically evaluate the accuracy of the forecasts build a custom model

This workshop provides hands-on experience using SAS^® Enterprise Miner^™ high-performance nodes. Workshop participants will do the following: learn the similarities and differences between high-performance nodes and standard nodes build a project flow using high-performance nodes extract and save a score code for model deployment

This workshop provides hands-on experience using SAS^® Office Analytics. Workshop participants will complete the following tasks: use SAS^® Enterprise Guide^® to access and analyze data create a stored process that can be shared across an organization access and analyze data sources and stored processes using the SAS^® Add-In for Microsoft Office

This workshop provides hands-on experience with SAS^® Visual Analytics. Workshop participants will do the following: explore data with SAS^® Visual Analytics Explorer design reports with SAS^® Visual Analytics Designer

This workshop provides hands-on experience using SAS^® Text Miner Workshop participants will do the following: read a collection of text documents and convert them for use by SAS Text Miner using the Text Import node use the simple query language supported by the Text Filter node to extract information from a collection of documents use the Text Topic node to identify the dominant themes and concepts in a collection of documents use the Text Rule Builder node to classify documents that have pre-assigned categories

Due to XML's growing role in data interchange, it is increasingly important for SAS^® programmers to become proficient with SAS technologies and techniques for creating and consuming XML. The current work expands on a SAS^® Global Forum 2013 presentation that dealt with these topics providing additional examples of using XML maps to read and write XML files and using the Output Delivery System (ODS) to create custom tagsets for generating XML.

Traditionally, Java web applications interact with back-end databases by means of JDBC/ODBC connections to retrieve and update data. With the growing need for real-time charting and complex analysis types of data representation on these types of web applications, SAS^® computing power can be put to use by adding a SAS web service layer between the application and the database. This paper shows how a SAS web service layer can be used to render data to a JAVA application in a summarized form using SAS^® Stored Processes. This paper also demonstrates how inputs can be passed to a SAS Stored Process based on which computations/summarizations are made before output parameter and/or output data streams are returned to the Java application. SAS Stored Processes are then deployed as SAS^® BI Web Services using SAS^® Management Console, which are available to the JAVA application as a URL. We use the SOAP method to interact with the web services. XML data representation is used as a communication medium. We then illustrate how RESTful web services can be used with JSON objects being the communication medium between the JAVA application and SAS in SAS^® 9.3. Once this pipeline communication between the application, SAS engine, and database is set up, any complex manipulation or analysis as supported by SAS can be incorporated into the SAS Stored Process. We then illustrate how graphs and charts can be passed as outputs to the application.

Using Lilypond typesetting software, you can write publication-grade music scores. The input for Lilypond is a text file that can be written once and then transferred to SAS^® for patterned repetition, so that you can cycle through patterns that occur in music. The author plays a sequence of notes and then writes this into Lilypond code. The sequence starts in the key of C with only a two-note sequence. Then the sequence is extended to three-, four-, then five-note sequences, always contained in one octave. SAS is then used to write the same code for all other eleven keys and in seven scale modes. The method is very simple and not advanced programming. Lookup files are used in the programming, demonstrating efficient lookup techniques. The result is a lengthy book or exercise for practicing music in a PDF file, and a sound source file in midi format is created that you can hear. This method shows how various programming languages can be used to write other programming languages.

Statistical mediation analysis is common in business, social sciences, epidemiology, and related fields because it explains how and why two variables are related. For example, mediation analysis is used to investigate how product presentation affects liking the product, which then affects the purchase of the product. Mediation analysis evaluates the mechanism by which a health intervention changes norms that then change health behavior. Research on mediation analysis methods is an active area of research. Some recent research in statistical mediation analysis focuses on extracting accurate information from small samples by using Bayesian methods. The Bayesian framework offers an intuitive solution to mediation analysis with small samples; namely, incorporating prior information into the analysis when there is existing knowledge about the expected magnitude of mediation effects. Using diffuse prior distributions with no prior knowledge allows researchers to reason in terms of probability rather than in terms of (or in addition to) statistical power. Using SAS^® PROC MCMC, researchers can choose one of two simple and effective methods to incorporate their prior knowledge into the statistical analysis, and can obtain the posterior probabilities for quantities of interest such as the mediated effect. This project presents four examples of using PROC MCMC to analyze a single mediator model with real data using: (1) diffuse prior information for each regression coefficient in the model, (2) informative prior distributions for each regression coefficient, (3) diffuse prior distribution for the covariance matrix of variables in the model, and (4) informative prior distribution for the covariance matrix.

How does the SAS^® server architecture fit within your IT infrastructure? What functional aspects does the architecture support? This session helps attendees understand the logical server topology of the SAS technology stack: resource and process management in-memory architecture in-database processing The session also discusses process flows from data acquisition through analytical information to visual insight. IT architects, data administrators, and IT managers from all industries should leave with an understanding of how SAS has evolved to better fit into the IT enterprise and to help IT's internal customers make better decisions.

To get the full benefit from PROC REPORT, the savvy programmer needs to master ACROSS usage and the COMPUTE block. Timing issues with PROC REPORT and ABSOLUTE column references can unlock the power of PROC REPORT. This presentation shows how to make the most of ACROSS usage with PROC REPORT. Use PROC REPORT instead of multiple TRANSPOSE steps. Find out how to use character variables with ACROSS. Learn how to impact the column headings for ACROSS usage items. Learn how to use aliases. Find out how to perform rowwise trafficlighting and trafficlighting based on multiple conditions.
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SAS^® has a number of procedures for smoothing scatter plots. In this tutorial, we review the nonparametric technique called LOESS, which estimates local regression surfaces. We review the LOESS procedure and then compare it to a parametric regression methodology that employs restricted cubic splines to fit nonlinear patterns in the data. Not only do these two methods fit scatterplot data, but they can also be used to fit multivariate relationships.

The scatter plot is a basic tool for examining the relationship between two variables. While the basic plot is good, enhancements can make it better. In addition, there might be problems of overplotting. In this paper, I cover ways to create basic and enhanced scatter plots and to deal with overplotting.

Linear regression has been a widely used approach in social and medical sciences to model the association between a continuous outcome and the explanatory variables. Assessing the model assumptions, such as linearity, normality, and equal variance, is a critical step for choosing the best regression model. If any of the assumptions are violated, one can apply different strategies to improve the regression model, such as performing transformation of the variables or using a spline model. SAS^® has been commonly used to assess and validate the postulated model and SAS^® 9.3 provides many new features that increase the efficiency and flexibility in developing and analyzing the regression model, such as ODS Statistical Graphics. This paper aims to demonstrate necessary steps to find the best linear regression model in SAS 9.3 in different scenarios where variable transformation and the implementation of a spline model are both applicable. A simulated data set is used to demonstrate the model developing steps. Moreover, the critical parameters to consider when evaluating the model performance are also discussed to achieve accuracy and efficiency.

All successful organizations seek ways of communicating the identity of subject matter experts to employees. This information exists as common knowledge when an organization is first starting out, but the common knowledge becomes fragmented as the organization grows. SAS^® Text Analytics can be used on an organization's internal unstructured data to reunite these knowledge fragments. This paper demonstrates how to extract and surface this valuable information from within an organization. First, the organization s unstructured textual data are analyzed by SAS^® Enterprise Content Categorization to develop a topic taxonomy that associates subject matter with subject matter experts in the organization. Then, SAS Text Analytics can be used successfully to build powerful semantic models that enhance an organization's unstructured data. This paper shows how to use those models to process and deliver real-time information to employees, increasing the value of internal company information.

Business analysts commonly use Microsoft Excel with the SAS^® System to answer difficult business questions. While you can use these applications independently of each other to obtain the information you need, you can also combine the power of those applications, using the SAS Output Delivery System (ODS) tagsets, to completely automate the process. This combination delivers a more efficient process that enables you to create fully functional and highly customized Excel worksheets within SAS. This paper starts by discussing common questions and problems that SAS Technical Support receives from users when they try to generate Excel worksheets. The discussion continues with methods for automating Excel worksheets using ODS tagsets and customizing your worksheets using the CSS style engine and extended tagsets. In addition, the paper discusses tips and techniques for moving from the current MSOffice2K and ExcelXP tagsets to the new Excel destination, which generates output in the native Excel 2010 format.

SAS^® OLAP technology is used to organize and present summarized data for business intelligence applications. It features flexible options for creating and storing aggregations to improve performance and brings a powerful multi-dimensional approach to querying data. This paper focuses on managing security features available to OLAP cubes through the combination of SAS metadata and MDX logic.

Security-conscious organizations have rigorous IT regulations, especially when company data is available on the move. This paper explores the options available to secure a deployment of SAS^® Mobile BI with SAS^® Visual Analytics. The setup ensures encrypted communication from remote mobile clients all the way to backend servers. Additionally, the integration of SAS Mobile BI with third-party Mobile Device Management (MDM) software and Virtual Private Network (VPN) technology enable you to place several layers of security and access control to your data. The paper also covers the out-of-the box security features of the SAS Mobile BI and SAS Visual Analytics administration applications to help you close the loop on all possible areas of exploitation.

Even if you are familiar with security considerations for SAS^® BI deployments, such as metadata and file system permissions, there are additional security aspects to consider when securing any environment that includes SAS^® Visual Analytics. These include files and permissions to the grid machines in a distributed environment, permissions on the SAS^® LASR™ Analytic Servers, and interactions with existing metadata types. We approach these security aspects from the perspective of an administrator who is securing the environment for himself, a data builder, and a report consumer.

Universities strive to be competitive in the quality of education as well as cost of attendance. Peer institutions are selected to make comparisons pertaining to academics, costs, and revenues. These comparisons lead to strategic decisions and long-range planning to meet goals. The process of finding comparable institutions could be completed with cluster analysis, a statistical technique. Cluster analysis places universities with similar characteristics into groups or clusters. A process to determine peer universities will be illustrated using PROC STANDARD, PROC FASTCLUS, and PROC CLUSTER.

Multiple imputation, a popular strategy for dealing with missing values, usually assumes that the data are missing at random (MAR). That is, for a variable X, the probability that an observation is missing depends only on the observed values of other variables, not on the unobserved values of X. It is important to examine the sensitivity of inferences to departures from the MAR assumption, because this assumption cannot be verified using the data. The pattern-mixture model approach to sensitivity analysis models the distribution of a response as the mixture of a distribution of the observed responses and a distribution of the missing responses. Missing values can then be imputed under a plausible scenario for which the missing data are missing not at random (MNAR). If this scenario leads to a conclusion different from that of inference under MAR, then the MAR assumption is questionable. This paper reviews the concepts of multiple imputation and explains how you can apply the pattern-mixture model approach in the MI procedure by using the MNAR statement, which is new in SAS/STAT^® 13.1. You can specify a subset of the observations to derive the imputation model, which is used for pattern imputation based on control groups in clinical trials. You can also adjust imputed values by using specified shift and scale parameters for a set of selected observations, which are used for sensitivity analysis with a tipping-point approach.

The Purchasing Department is considering contracting with your team for a new SAS^® Enterprise BI application. He's already met with SAS^® and seen the sales pitch, and he is very interested. But the manager is a tightwad and not sure about spending the money. Also, he wants his team to be the primary developers for this new application. Before investing his money on training, programming, and support, he would like a proof-of-concept. This paper will walk you through the seven steps to create a SAS Enterprise BI POC project: Develop a kick-off meeting including a full demo of the SAS Enterprise BI tools. Set up your UNIX file systems and security. Set up your SAS metadata ACTs, users, groups, folders, and libraries. Make sure the necessary SAS client tools are installed on the developers machines. Hold a SAS Enterprise BI workshop to introduce them to the basics, including SAS^® Enterprise Guide^®, SAS^® Stored Processes, SAS^® Information Maps, SAS^® Web Report Studio, SAS^® Information Delivery Portal, and SAS^® Add-In for Microsoft Office, along with supporting documentation. Work with them to develop a simple project, one that highlights the benefits of SAS Enterprise BI and shows several methods for achieving the desired results. Last but not least, follow up! Remember, your goal is not to launch a full-blown application. Instead, we ll strive toward helping them see the potential in your organization for applying this methodology.

SAS^® Visual Analytics enables you to conduct ad hoc data analysis, visually explore data, develop reports, and then share insights through the web and mobile tablet apps. You can now also share your insights with colleagues using the SAS^® Office Analytics integration with Microsoft Excel, Microsoft Word, Microsoft PowerPoint, Microsoft Outlook, and Microsoft SharePoint. In addition to opening and refreshing reports created using SAS Visual Analytics, a new SAS^® Central view enables you to manage and comment on your favorite and recent reports from your Microsoft Office applications. You can also view your SAS Visual Analytics results in SAS^® Enterprise Guide^®. Learn more about this integration and what's coming in the future in this breakout session.

SAS^® continues to expand and improve its reporting capability. With new SAS^® 9.4 enhancements in ODS (Output Delivery System), the opportunity to create stunning reports has expanded even further. If you are charged with creating relevant, informative, easy-to-read reports for clients or administrators, then the ODS Report Writing Interface, ODS LAYOUT enhancements, and the new ODSTEXT procedure are important tools to use. These tools allow you to create reports in a smart, eye-catching format that can be turned around quite quickly and programmed to provide optimum flexibility. How many times have you worked hours to tweak and fine-tune a report directly in Microsoft Excel, Microsoft Word, Microsoft Power Point or some other similar software only to be asked for a quick update , which would then take hours to recreate because you are manually transferring data? Do you ever dread receiving the compliment, This is really wonderful information!!!! because you know it will be followed by Can you run this for EVERY region? Well, dread no more, because when you harness the power of SAS^® ODS, you can create first-rate, flexible, fabulous reports! Join me as I share with you two real-world examples of ODS capabilities using (1) a marketing piece I designed to help the president of our university spotlight county- and region-specific data as he recruited across the state and (2) our academic program review form, a multi-page report that outputs to Word so that program coordinators can add personalized commentary to support their program s effectiveness.

Traditional approaches for data integration require an investment in both tooling for automation and in skills needed to work with and manage those tools. Organizations want to leverage their current investments in data integration tooling and apply them to current technology such as big data and cloud computing without the need to hire new employees or retraining existing ones. This paper will introduce and demonstrate the intuitive and easy-to-use interface of an all-new code generation environment from SAS that simplifies the effort to move, transform, and clean data in place so that anyone can do it. Whether the data is in SAS, a relational database, in a Hadoop cluster, in-memory, or in the cloud, this paper will show how users of any skill level will be able to define and direct powerful integration algorithms that allow work to pushed to and executed from anywhere without the need to learn any specialized skills such as writing MapReduce code.

Companies in the insurance and banking industries need to model the frequency and severity of adverse events every day. Accurate modeling of risks and the application of predictive methods ensure the liquidity and financial health of portfolios. Often, the modeling involves computationally intensive, large-scale simulation. SAS/ETS^® provides high-performance procedures to assist in this modeling. This paper discusses the capabilities of the HPCOUNTREG and HPSEVERITY procedures, which estimate count and loss distribution models in a massively parallel processing environment. The loss modeling features have been extended by the new HPCDM procedure, which simulates the probability distribution of the aggregate loss by compounding the count and severity distribution models. PROC HPCDM also analyzes the impact of various future scenarios and parameter uncertainty on the distribution of the aggregate loss. This paper steps through the entire modeling and simulation process that is useful in the insurance and banking industries.
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Big data is all the rage these days, with the proliferation of data-accumulating electronic gadgets and instrumentation. At the heart of big data analytics is the MapReduce programming model. As a framework for distributed computing, MapReduce uses a divide-and-conquer approach to allow large-scale parallel processing of massive data. As the name suggests, the model consists of a Map function, which first splits data into key-value pairs, and a Reduce function, which then carries out the final processing of the mapper outputs. It is not hard to see how these functions can be simulated with the SAS^® hash objects technique, and in reality, implemented in the new SAS^® DS2 language. This paper demonstrates how hash object programming can handle data in a MapReduce fashion and shows some potential applications in physics, chemistry, biology, and finance.

For electricity retailer and distribution companies, the introduction of smart-meter technologies has been a key investment, reducing the significant costs associated with meter reading. Electricity companies continue to look for ways to generate a dividend from them in other ways. This presentation looks at selected practical applications of smart-meter data: forecasting using smart-meter data as inputs customer segmentation revenue protection This presentation aims to show some techniques that can be used to effectively manage and analyze the large amounts of data generated by these devices in order to generate business value.

Distributing SAS^® software to a large number of machines can be challenging at best and exhausting at worst. Common areas of concern for installers are silent automation, network traffic, ease of setup, standardized configurations, maintainability, and simply the sheer amount of time it takes to make the software available to end users. We describe a variety of techniques for easing the pain of provisioning SAS software, including the new standalone SAS^® Enterprise Guide^® and SAS^® Add-in for Microsoft Office installers, as well as the tried and true SAS^® Deployment Wizard record and playback functionality. We also cover ways to shrink SAS Software Depots, like the new 'subsetting recipe' feature, in order to ease scenarios requiring depot redistribution. Finally, we touch on alternate methods for workstation access to SAS client software, including application streaming, desktop virtualization, and Java Web Start.

All the documentation about the creation of graphs with SAS^® software states that ODS Graphics is not intended to replace SAS/GRAPH^®. However, ODS Graphics is included in the Base SAS^® license from SAS^® 9.3, but SAS/GRAPH still requires an additional component license, so there is definitely a financial incentive to convert to ODS Graphics. This paper gives examples that can be used to replace commonly created SAS/GRAPH plots, and highlights the small number of plots that are still very difficult, or impossible, to create in ODS Graphics.

Have you ever needed to use dates as values to loop through a table? For example, how many events occurred by 1, 2 , 3 & n months ahead? Maybe you just changed the dates manually and re-ran the query n times? This is a common need in economic and behavioral sciences. This presentation demonstrates how to create a table of dates that can be used with SAS^® macro variables to loop through a table. Using this dates table in combination with the SAS DO loop ensures accuracy and saves time.

Businesses today are inundated with unstructured data not just social media but books, blogs, articles, journals, manuscripts, and even detailed legal documents. Manually managing unstructured data can be time consuming and frustrating, and might not yield accurate results. Having an analyst read documents often introduces bias because analysts have their own experiences, and those experiences help shape how the text is interpreted. The fact that people become fatigued can also impact the way that the text is interpreted. Is the analyst as motivated at the end of the day as they are at the beginning? Data science involves using data management, analytical, and visualization strategies to uncover the story that the data is trying to tell in a more automated fashion. This is important with structured data but becomes even more vital with unstructured data. Introducing automated processes for managing unstructured data can significantly increase the value and meaning gleaned from the data. This paper outlines the data science processes necessary to ingest, transform, analyze, and visualize three Star Wars movie scripts: A New Hope, The Empire Strikes Back, and Return of the Jedi. It focuses on the need to create structure from unstructured data using SAS^® Data Management, SAS^® Text Miner, and SAS^® Content Categorization. The results are featured using SAS^® Visual Analytics.

One beautiful graph provides visual clarity of data summaries reported in tables and listings. Waterfall graphs show, at a glance, the increase or decrease of data analysis results from various industries. The introduction of SAS^® 9.2 ODS Statistical Graphics enables SAS^® programmers to produce high-quality results with less coding effort. Also, SAS programmers can create sophisticated graphs in stylish custom layouts using the SAS^® 9.3 Graph Template Language and ODS style template. This poster presents two sets of example waterfall graphs in the setting of clinical trials using SAS^® 9.3 and later. The first example displays colorful graphs using new SAS 9.3 options. The second example displays simple graphs with gray-scale color coding and patterns. SAS programmers of all skill levels can create these graphs on UNIX or Windows.

Systematic reviews have become increasingly important in healthcare, particularly when there is a need to compare new treatment options and to justify clinical effectiveness versus cost. This paper describes a method in SAS/STAT^® 9.2 for computing weighted averages and weighted standard deviations of clinical variables across treatment options while correctly using these summary measures to make accurate statistical inference. The analyses of data from systematic reviews typically involve computations of weighted averages and comparisons across treatment groups. However, the application of the TTEST procedure does not currently take into account weighted standard deviations when computing p-values. The use of a default non-weighted standard deviation can lead to incorrect statistical inference. This paper introduces a method for computing correct p-values using weighted averages and weighted standard deviations. Given a data set containing variables for three treatment options, we want to make pairwise comparisons of three independent treatments. This is done by creating two temporary data sets using PROC MEANS, which yields the weighted means and weighted standard deviations. Subsequently, we then perform a t-test on each temporary data set.The resultant data sets containing all comparisons of each treatment options are merged and then transposed to obtain the necessary statistics. The resulting output provides pairwise comparisons of each treatment option and uses the weighted standard deviations to yield the correct p-values in a desired format. This method allows the use of correct weighted standard deviations using PROC MEANS and PROC TTEST in summarizing data from a systematic review while providing correct p-values.

Contrasting two sets of textual data points out important differences. For example, consider social media data that have been collected on the race between incumbent Kay Hagan and challenger Thom Tillis in the 2014 election for the seat of US Senator from North Carolina. People talk about the candidates in different terms for different topics, and you can extract the words and phrases that are used more in messages about one candidate than about the other. By using SAS^® Sentiment Analysis on the extracted information, you can discern not only the most important topics and sentiments for each candidate, but also the most prominent and distinguishing terms that are used in the discussion. Find out if Republicans and Democrats speak different languages!

Westat utilizes SAS^® software as a core capability for providing clients in government and private industry with analysis and characterization of survey data. Staff programmers, analysts, and statisticians use SAS to manage, store, and analyze client data, as well as to produce tabulations, reports, graphs, and summary statistics. Because SAS is so widely used at Westat, the organization has built a comprehensive infrastructure to support its deployment and use. This paper provides an overview of Westat's SAS support infrastructure, which supplies resources that are aimed at educating staff, strengthening their SAS skills, providing SAS technical support, and keeping the staff on the cutting edge of SAS programming techniques.

One in every four people dies of heart disease in the United States, and stress is an important factor which contributes towards a cardiac event. As the condition of the heart gradually worsens with age, the factors that lead to a myocardial infarction when the patients are subjected to stress are analyzed. The data used for this project was obtained from a survey conducted through the Department of Biostatistics at Vanderbilt University. The objective of this poster is to predict the chance of survival of a patient after a cardiac event. Then by using decision trees, neural networks, regression models, bootstrap decision trees, and ensemble models, we predict the target which is modeled as a binary variable, indicating whether a person is likely to survive or die. The top 15 models, each with an accuracy of over 70%, were considered. The model will give important survival characteristics of a patient which include his history with diabetes, smoking, hypertension, and angioplasty.

In the day-to-day operations of a Biostatistics and Statistical Programming department, we are often tasked with generating reports in the form of tables, listings, and figures (TLFs). Some requests come in the form of a small number of TLFs, whereas others are more substantial in magnitude. Regardless, creating a single document for distribution and review might be required after all TLFs have been completed. A common setting in the pharmaceutical industry is to develop SAS^® code in which individual programs generate one or more TLFs in some standard formatted output such as RTF or PDF with a common look and feel. Furthermore, programs are developed over time, with the production run in batch mode. The result is a set of TLFs completed at different times. Creation of a final (single) document with a properly sectioned and hyperlinked Table of Contents, as well as dynamic page numbering, might be wanted. The ability to deliver a single document greatly simplifies document management and electronic review for many users. Many options have been proposed that post-process individual RTF or PDF files. An alternative approach, which uses ODS Document, is introduced. Unlike many other techniques, ODS Document uses intermediate files called 'item stores' that are independent of the ODS destination. This technique has proven successful across multiple projects in our specific setting and continues to show promise in other applications as well.

Before you can analyze your big data, you need to prepare the data for analysis. This paper discusses capabilities and techniques for using the power of SAS^® to prepare big data for analytics. It focuses on how a SAS user can write code that will run in a Hadoop cluster and take advantage of the massive parallel processing power of Hadoop.

SAS^® platform installations are large, complex, growing, and ever-changing enterprise systems that support many diverse groups of users and content. A reliable metadata security implementation is critical for providing access to business resources in a methodical, organized, partitioned, and protected manner. With natural changes to users, groups, and folders from an organization s day-to-day activities, deviations from an original metadata security plan are very likely and can put protected resources at risk. Regular security testing can ensure compliance, but, given existing administrator commitments and the time consuming nature of manual testing procedures, it doesn't tend to happen. This paper discusses concepts and outlines several example test specifications from an automated metadata security testing framework being developed by Metacoda. With regularly scheduled, automated testing, using a well-defined set of test rules, administrators can focus on their other work, and let alerts notify them of any deviations from a metadata security test specification.

With smartphone and mobile apps market developing so rapidly, the expectations about effectiveness of mobile applications is high. Marketers and app developers need to analyze huge data available much before the app release, not only to better market the app, but also to avoid costly mistakes. The purpose of this poster is to build models to predict the success rate of an app to be released in a particular category. Data has been collected for 540 android apps under the Top free newly released apps category from https://play.google.com/store . The SAS^® Enterprise Miner^™ Text Mining node and SAS^® Sentiment Analysis Studio are used to parse and tokenize the collected customer reviews and also to calculate the average customer sentiment score for each app. Linear regression, neural, and auto-neural network models have been built to predict the rank of an app by considering average rating, number of installations, total number of reviews, number of 1-5 star ratings, app size, category, content rating, and average customer sentiment score as independent variables. A linear regression model with least Average Squared Error is selected as the best model, and number of installations, app maturity content are considered as significant model variables. App category, user reviews, and average customer sentiment score are also considered as important variables in deciding the success of an app. The poster summarizes the app success trends across various factors and also introduces a new SAS^® macro %getappdata, which we have developed for web crawling and text parsing.

Global businesses must react to daily changes in market conditions over multiple geographies and industries. Consuming reputable daily economic reports assists in understanding these changing conditions, but requires both a significant human time commitment and a subjective assessment of each topic area of interest. To combat these constraints, Dow's Advanced Analytics team has constructed a process to calculate sentence-level topic frequency and sentiment scoring from unstructured economic reports. Daily topic sentiment scores are aggregated to weekly and monthly intervals and used as exogenous variables to model external economic time series data. These models serve to both validate the relationship between our sentiment scoring process and also as near-term forecasts where daily or weekly variables are unavailable. This paper will first describe our process of using SAS^® Text Miner to import and discover economic topics and sentiment from unstructured economic reports. The next section describes sentiment variable selection techniques that use SAS/STAT^®, SAS/ETS^®, and SAS^® Enterprise Miner^™ to generate similarity measures to economic indices. Our process then uses ARIMAX modeling in SAS^® Forecast Studio to create economic index forecasts with topic sentiments. Finally, we show how the sentiment model components are used as a matrix of economic key performance indicators by topic and geography.

Nowadays, in the Big Data era, Business Intelligence Departments collect, store, process, calculate, and monitor massive amounts of data. Nevertheless, sometimes hundreds of metrics built on the structured data are inefficient to explain why the offered deal sold better or worse than expected. The answer might be found in text data that every company owns and yet is not aware of its possible usage or neglects its value. This project shows text mining methods, implemented in SAS^® Text Miner 12.1, that enable the determination of a deal's success or failure factors based on in-house or Internet-scattered customers' views and opinions. The study is conducted on data gathered from Groupon Sp. z o.o. (Polish business unit) - e-commerce company, as it is assumed that the market is by and large a customer-driven environment.

'Can I have that in Excel?' This is a request that makes many of us shudder. Now your boss has discovered Microsoft Excel pivot tables. Unfortunately, he has not discovered how to make them. So you get to extract the data, massage the data, put the data into Excel, and then spend hours rebuilding pivot tables every time the corporate data is refreshed. In this workshop, you learn to be the armchair quarterback and build pivot tables without leaving the comfort of your SAS^® environment. In this workshop, you learn the basics of Excel pivot tables and, through a series of exercises, you learn how to augment basic pivot tables first in Excel, and then using SAS. No prior knowledge of Excel pivot tables is required.

In a clinical study, we often set up multiple hypotheses with regard to the cost of getting study result. However, the multiplicity problem arises immediately when they are performed in a univariate manner. Some methods to control the rate of the overall type I error are applied widely, and they are discussed in this paper, except the methodology, we will introduce its application in one study case and provide the SAS^® code.

Once upon a time, a writer compared a desert to a labyrinth. A desert has no walls or stairways, but you can still find yourself utterly lost in it. And oftentimes, when you think you found that oasis you were looking for, what you are really seeing is an illusion, a mirage. Similarly, logical fallacies and misleading data patterns can easily deceive the unaware data explorer. In this paper, we discuss how they can be recognized and neutralized with the power of the SAS^® Visual Analytics Explorer. Armed with this knowledge, you will be able to safely navigate the dunes to find true insights and avoid false conclusions.

We continually work with our hardware partners to establish best practices with regard to tuning the latest hardware components that are released each year. This paper goes over the latest tuning guidelines for your hardware infrastructure, including your host computer system, operating system, and complete I/O infrastructure (from the computer host and network adapters down through the physical storage). Our findings are published in SAS^® papers on the SAS website, support.sas.com, with updates posted to the SAS Administration blog.

For decades, SAS^® has been the cornerstone of many organizations for business reporting. In more recent times, the ability to quickly determine the performance of an organization through the use of dashboards has become a requirement. Different ways of providing dashboard capabilities are discussed in this paper: using out-of-the-box solutions such as SAS^® Visual Analytics and SAS^® BI Dashboard, through to alternative solutions using SAS^® Stored Processes, batch processes, and SAS^® Integration Technologies. Extending the available indicators is also discussed, using Graph Template Language and KPI indicators provided with Base SAS^®, as well as alternatives such as Google Charts and Flash objects. Real-world field experience, problem areas, solutions, and tips are shared, along with live examples of some of the different methods.

The FORMAT procedure in SAS^® is a very powerful and productive tool, yet many beginning programmers rarely make use of it. The FORMAT procedure provides a convenient way to do a table lookup in SAS. User-generated FORMATS can be used to assign descriptive labels to data values, create new variables, and find unexpected values. PROC FORMAT can also be used to generate data extracts and to merge data sets. This paper provides an introductory look at PROC FORMAT for the beginning user and provides sample code that illustrates the power of PROC FORMAT in a number of applications. Additional examples and applications of PROC FORMAT can be found in the SAS^® Press book titled 'The Power of PROC FORMAT.'

The SAS^® Enterprise Guide^® Query Builder is one of the most powerful components of the software. It enables a user to bring in data, join, drop and add columns, compute new columns, sort, filter data, leverage the advanced expression builder, change column attributes, and more! This presentation provides an overview of the major features of this powerful tool and how to leverage it every day.

Raking (iterative proportional fitting) is a procedure that takes sampling weights from complex sample surveys and adjusts them so that they add to known control totals. This process reduces variance and adjusts for undercoverage. But raking in multiple dimensions can lead to extreme weights, which increase variance. Trimming is another sample weighting procedure that reduces extreme weights to cutoffs, thereby improving variance properties while potentially introducing bias. The RAKE-TRIM macro combines raking and trimming in an iterative algorithm to achieve these two goals simultaneously. The raking reduces the bias potential from trimming, and the trimming reduces the variance inflation from raking. When convergence occurs, the final weights aggregate to the control totals, as well as respect the trimming limits. SAS^® macros are well suited for this kind of envelope program: the larger macro consists of the integration of component macros that were developed for other applications. A parameter specification sheet enables users to provide all of the parameters needed to define the algorithm for their particular situation, and, if necessary, to alter the parameters to facilitate convergence. Diagnostics are included when convergence fails. Microsoft Excel tables are imported to provide the cell structure and are exported to provide statistics for the algorithm s results. This RAKE-TRIM macro was first developed in 2010 for the 2009 National Household Transportation Survey and has been used in other studies as well. The paper describes the algorithm and discusses our experiences with it.

Direct marketing is the practice of delivering promotional messages directly to potential customers on an individual basis rather than by using mass medium. In this project, we build a finely tuned response model that helps a financial services company to select high-quality receptive customers for their future campaigns and to identify the important factors that influence marketing to effectively manage their resources. This study was based on the customer solicitation center s marketing campaign data (45,211 observations and 18 variables) available on UC Irvine's web site with attributes of present and past campaign information (communication type, contact duration, previous campaign outcome, and so on) and customer s personal and banking information. As part of data preparation, we had performed mean imputation to handle missing values and categorical recoding for reducing levels of class variables. In this study, we had built several predictive models using the SAS^® Enterprise Miner^™ models Decision Tree, Neural Network, Logistic Regression, and SVM to predict whether the customer responds to the loan offer by subscribing. The results showed that the Stepwise Logistic Regression model was the best when chosen based on the misclassification rate criteria. When the top 3 decile customers were selected based on the best model, the cumulative response rate was 14.5% in contrast to the baseline response rate of 5%. Further analysis showed that the customers are more likely to subscribe to the loan offer if they have the following characteristics: never been contacted in the past, no default history, and provided cell phone as primary contact information.

This is the way I have always done it and it works fine for me. Have you heard yourself or others say this when someone suggests a new technique to help solve a problem? Most of us have a set of tricks and techniques from which we draw when starting a new project. Over time we might overlook newer techniques because our old toolkit works just fine. Sometimes we actively avoid new techniques because our initial foray leaves us daunted by the steep learning curve to mastery. For me, the PRX functions and the SAS^® hash object fell into this category. In this workshop, we address possible objections to learning to use the SAS hash object. We start with the fundamentals of setting up the hash object and work through a variety of practical examples to help you master this powerful technique.

Applying models to analyze sports data has always been done by teams across the globe. The film Moneyball has generated much hype about how a sports team can use data and statistics to build a winning team. The objective of this poster is to use the model comparison algorithm of SAS^® Enterprise Miner^™ to pick the best model that can predict the outcome of a soccer game. It is hence important to determine which factors influence the results of a game. The data set used contains input variables about a team s offensive and defensive abilities and the outcome of a game is modeled as a target variable. Using SAS Enterprise Miner, multinomial regression, neural networks, decision trees, ensemble models and gradient boosting models are built. Over 100 different versions of these models are run. The data contains statistics from the 2012-13 English premier league season. The competition has 20 teams playing each other in a home and away format. The season has a total of 380 games; the first 283 games are used to predict the outcome of the last 97 games. The target variable is treated as both nominal variable and ordinal variable with 3 levels for home win, away win, and tie. The gradient boosting model is the winning model which seems to predict games with 65% accuracy and identifies factors such as goals scored and ball possession as more important compared to fouls committed or red cards received.

In the traveling salesman problem, a salesman must minimize travel distance while visiting each of a given set of cities exactly once. This paper uses the SAS/OR^® OPTMODEL procedure to formulate and solve the traveling baseball fan problem, which complicates the traveling salesman problem by incorporating scheduling constraints: a baseball fan must visit each of the 30 Major League ballparks exactly once, and each visit must include watching a scheduled Major League game. The objective is to minimize the time between the start of the first attended game and the end of the last attended game. One natural integer programming formulation involves a binary decision variable for each scheduled game, indicating whether the fan attends. But a reformulation as a side-constrained network flow problem yields much better solver performance.

Identifying claim fraud using predictive analytics represents a unique challenge. 1. Predictive analytics generally requires that you have a target variable which can be analyzed. Fraud is unique in this regard in that there is a lot of fraud that has occurred historically that has not been identified. Therefore, the definition of the target variable is difficult. 2.There is also a natural assumption that the past will bear some resemblance to the future. In the case of fraud, methods of defrauding insurance companies change quickly and can make the analysis of a historical database less valuable for identifying future fraud. 3. In an underlying database of claims that may have been determined to be fraudulent by an insurance company, there is many times an inconsistency between different claim adjusters regarding which claims are referred for investigation. This inconsistency can lead to erroneous model results due to data that is not homogenous. This paper will demonstrate how analytics can be used in several ways to help identify fraud: 1. More consistent referral of suspicious claims 2. Better identification of new types of suspicious claims 3. Incorporating claim adjuster insight into the analytics results. As part of this paper, we will demonstrate the application of several approaches to fraud identification: 1. Clustering 2. Association analysis 3. PRIDIT (Principal Component Analysis of RIDIT scores).

HTML5 has become the de facto standard for web applications. As a result, the lingua franca object notation of the web services that the web applications call has switched from XML to JSON. JSON is remarkably easy to parse in JavaScript, but so far SAS doesn't have any native JSON parsers. The Facebook Graph API dropped XML support a few years ago. This paper shows how we can parse the JSON in SAS by calling an external script, using PROC GROOVY to parse it inside of SAS, or by parsing the JSON manually with a DATA step. We'll extract the data from the Facebook Graph API and import it into an OLAP data mart to report and analyze a marketing campaign's effectiveness.

This new SAS^® tool is a two-dimensional color chart for visualizing changes in a population or in a system over time. Data for one point in time appear as a thin horizontal band of color. Bands for successive periods are stacked up to make a two-dimensional plot, with the vertical direction showing changes over time. As a system evolves over time, different kinds of events have different characteristic patterns. Creation of Time Contour plots is explained step-by-step. Examples are given in astrostatistics, biostatistics, econometrics, and demographics.

Changes in health insurance and other industries often have a spatial component. Maps can be used to convey this type of information to the user more quickly than tabular reports and other non-graphical formats. SAS^® provides programmers and analysts with the tools to not only create professional and colorful maps, but also the ability to display spatial data on these maps in a meaningful manner that aids in the understanding of the changes that have transpired. This paper illustrates the creation of a number of different maps for displaying change over time with examples from the health insurance arena.

SAS^® Management Console was designed to control and monitor virtually all of the parts and features of the SAS^® Intelligence Platform. However, administering even a small SAS^® Business Intelligence system can be a daunting task. This paper presents a few techniques that will help you simplify your administrative tasks and enable you and your user community to get the most out of your system. The SAS^® Metadata Server stores most of the information required to maintain and run the SAS Intelligence Platform, which is obviously the heart of SAS BI. It stores information about libraries, users, database logons, passwords, stored processes, reports, OLAP cubes, and a myriad of other information. Organization of this metadata is an essential part of an optimally performing system. This paper discusses ways of organizing the metadata to serve your organization well. It also discusses some of the key features of SAS Management Console and best practices that will assist the administrator in defining roles, promoting, archiving, backing up, securing, and simply just organizing the data so that it can be found and accessed easily by administrators and users alike.

No need to fret, Base SAS^® programmers. Converting to SAS^® Enterprise Guide^® is a breeze, and it provides so many advantages. Coding remote connections to SAS^® servers is a thing of the past. Generate WYSIWYG prompts to increase the usage of the SAS code and to create reports and SAS^® Stored Processes to share easily with people who don t use SAS Enterprise Guide. The first and most important thing, however, is to change the default options and preferences to tame SAS Enterprise Guide, making it behave similar to your Base SAS ways. I cover all of these topics and provide demos along the way.

As a longtime Base SAS^® programmer, whether to use a different application for programming is a constant question when powerful applications such as SAS^® Enterprise Guide^® are available. This paper provides some important tips for a programmer, such as the best way to use the code window and how to take advantage of system-generated code in SAS Enterprise Guide 5.1. This paper also explains the differences between some of the functions and procedures in Base SAS and SAS Enterprise Guide. It highlights features in SAS Enterprise Guide such as process flow, data access management, and report automation, including formatting using XML tag sets.

This paper gives you a better idea of how and where to use the record lookup functions to locate observations where a variable has some characteristic. Various related functions are illustrated to search numeric and character values in this process. Code is shown with time comparisons. I will discuss three possible ways to retrieve records using the SAS^® DATA step, PROC SQL, and Perl regular expressions. Real and CPU time processing issues will be highlighted when comparing to retrieve records using these methods. Although the program is written for the PC using SAS^® 9.2 in a Windows XP 32-bit environment, all the functions are applicable to any system. All the tools discussed are in Base SAS^®. The typical attendee or reader will have some experience in SAS, but not a lot of experience dealing with large amount of data.

Two new production features offered in the Output Delivery System (ODS) in SAS^® 9.4 are ODS LAYOUT and the ODS Report Writing Interface. This one-two punch gives you power and flexibility in structuring your SAS^® output. What are the strengths for each? How do they differ? How do they interact? This paper highlights the similarities and differences between the two and illustrates the advantages of using them together. Why go twelve rounds? Make your report a knockout with ODS LAYOUT and the Report Writing Interface.

This paper introduces basic-to-advanced strategies and syntax, the tools of the SAS^® trade, that enable client-quality PDF output to be delivered through a production system of macro programs. A variety of PROC REPORT output with proven client value serves to illustrate a discussion of the fundamental syntax used to create and share formats, macro programs, PROC REPORT output, inline styles, and style templates. The syntax is integrated into basic macro programs that demonstrate the the core functionality of the reporting system. Later sections of the paper describe in detail the macro programs used to start and end a PDF: (a) programs to save all current titles, footnotes, and option settings, establish standard titles, footnotes and option settings, and initially create the PDF document; and (b) programs to create a final standard data documentation page, end the PDF, and restore all original titles, footnotes, and option settings. The paper also shows how macro programs enable the setting of inline styles at the global, macro program, and macro program call-levels. The paper includes the style template syntax and the complete PROC REPORT syntax generated by the macro programs, and is designed for the intermediate to advanced SAS programmer using Foundation SAS^® for Release 9.2 on a Windows operating system.

When assisting SAS^® customers who are experiencing performance issues, we are often asked by the SAS users at a customer site for the top 10 guidelines to share with those who have taken on the role of system administrator or SAS administrator. This paper points you to where you can get more information regarding each of the guidelines and related details on the SAS website.

One of the most striking features separating SAS^® from other statistical languages is that SAS has native SQL (Structured Query Language) capacity. In addition to the merging or the querying that a SAS user commonly applies in daily practice, SQL significantly enhances the power of SAS in descriptive statistics and data management. In this paper, we show reproducible examples to introduce 10 useful tips for the SQL procedure in the BASE module.

Do you often create SAS^® web applications? Do you need to update or retrieve values from a SAS data set and display them in a browser? Do you need to show the results of a SAS^® Stored Process in a browser? Are you finding it difficult to figure out how to pass parameters from a web page to a SAS Stored Process? If you answered yes to any of these questions, then look no further. Techniques shown in this paper include: How to take advantage of JavaScript and minimize PUT statements. How to call a SAS Stored Process from your web page by using JavaScript and XMLHTTPRequest. How to pass parameters from a web page to a SAS Stored Process and from a SAS Stored Process back to the web page. How to use simple Ajax to refresh and update a specific part of a web page without the need to reload the entire page. How to apply Cascading Style Sheets (CSS) on your web page. How to use some of the latest HTML5 features, like drag and drop. How to display run-time graphs in your web page by using STATGRAPH and PROC SGRENDER. This paper contains sample code that demonstrates each of the techniques.

SAS^® Add-In for Microsoft Office remains a popular tool for people who are not SAS^® programmers due to its easy interface with the SAS servers. In this session, you'll learn some of the many tricks that other organizations use for getting more value out of the tool.

The independent means t-test is commonly used for testing the equality of two population means. However, this test is very sensitive to violations of the population normality and homogeneity of variance assumptions. In such situations, Yuen s (1974) trimmed t-test is recommended as a robust alternative. The purpose of this paper is to provide a SAS^® macro that allows easy computation of Yuen s symmetric trimmed t-test. The macro output includes a table with trimmed means for each of two groups, Winsorized variance estimates, degrees of freedom, and obtained value of t (with two-tailed p-value). In addition, the results of a simulation study are presented and provide empirical comparisons of the Type I error rates and statistical power of the independent samples t-test, Satterthwaite s approximate t-test, and the trimmed t-test when the assumptions of normality and homogeneity of variance are violated.

As SAS^® professionals, we often wish our clients would make more use of the many excellent SAS tools at their disposal. However, it remains an indisputable fact that for many business users, Microsoft Excel is still their go-to application when it comes to carrying out any form of data analysis. There have been many attempts to integrate SAS and Excel, but none of these has up to now been entirely seamless. This paper addresses that problem by showing how, with a minimum of VBA (Visual Basic for Applications) code and by using the SAS Integrated Object Model (IOM) together with Microsoft s ActiveX Data Objects (ADO), we can create an Excel User Defined Function (UDF) that can accept parameters, carry out all data manipulations in SAS, and return the result to the spreadsheet in a way that is completely invisible to the user. They can nest or link these functions together just as if they were native Excel functions. We then go on to demonstrate how, using the same techniques, we can create small Excel applications that can perform sophisticated data analyses in SAS while not forcing users out of their Excel comfort zones.

As a retailer, your bottom line is determined by supply and demand. Are you supplying what your customer is demanding? Or do they have to go look somewhere else? Accurate allocation and size optimization mean your customer will find what they want more often. And that means more sales, higher profits, and fewer losses for your organization. In this session, Linda Canada will share how DSW went from static allocation models without size capability to precision allocation using intelligent, dynamic models that incorporate item plans and size optimization.

You don't have to be with the CIA to discover why your SAS^® stored process is producing clandestine results. In this talk, you will learn how to use prompts to get the results you want, work with the metadata to ensure correct results, and even pick up simple coding tricks to improve performance. You will walk away with a new decoder ring that allows you to discover the secrets of the SAS logs!

Understanding previous research in key domain areas can help R&D organizations focus new research in non-duplicative areas and ensure that future endeavors do not repeat the mistakes of the past. However, manual analysis of previous research efforts can prove insufficient to meet these ends. This paper highlights how a combination of SAS^® Text Analytics and SAS^® Visual Analytics can deliver the capability to understand key topics and patterns in previous research and how it applies to a current research endeavor. We will explore these capabilities in two use cases. The first will be in uncovering trends in publicly visible government funded research (SBIR) and how these trends apply to future research in nanotechnology. The second will be visualizing past research trends in publicly available NASA publications, and how these might impact the development of next-generation spacecraft.

SAS^® provides a wide variety of products and solutions that address analytics, data management, and reporting. It can be challenging to understand how the data and processes in a SAS deployment relate to each other and how changes in your processes affect downstream consumers. This paper presents visualization and reporting tools for lineage and impact analysis. These tools enable you to understand where the data for any report or analysis originates or how data is consumed by data management, analysis, or reporting processes. This paper introduces new capabilities to import metadata from third-party systems to provide lineage and impact analysis across your enterprise.

The DOW-loop is not official terminology that one can find in SAS^® documentation, but it has been well known and widely used among experienced SAS programmers. The DOW-loop was developed over a decade ago by a few SAS gurus, including Don Henderson, Paul Dorfman, and Ian Whitlock. A common construction of the DOW-loop consists of a DO-UNTIL loop with a SET and a BY statement within the loop. This construction isolates actions that are performed before and after the loop from the action within the loop, which results in eliminating the need for retaining or resetting the newly created variables to missing in the DATA step. In this talk, in addition to explaining the DOW-loop construction, we review how to apply the DOW-loop to various applications.

UniCredit Group is a large financial institution (G-Sifi) with a clear focus to develop and execute a data governance strategy. To deliver this focus, UniCredit implemented a robust environment to support the advanced analytics process that is directly connected to the Teradata Data Warehouse. This presentation highlights how UniCredit developed an analytic program for the Region Italy, covering the business needs in an integrated and highly governed environment. The CFO s aim is to use the analytical business tools for monitoring Sales Area and Business Network analysis with the adoption of SAS^® Visual Analytics on the Teradata Appliance for SAS^®, Model 720.

SAS^® Visual Analytics is a unique tool that provides both exploratory and predictive data analysis capabilities. As the visual part of the name suggests, the rendering of this analysis in the form of visuals (crosstabs, line charts, histograms, scatter plots, geo maps, treemaps, and so on) make this a very useful tool. Join me as I walk you down the path of exploring the capabilities of SAS Visual Analytics 6.3, starting with data stored in a desktop application as multiple Microsoft Excel files. Together, we import the data into SAS Visual Analytics, prepare the data using the data builder, load the data into SAS^® LASR™ Analytic Server, explore data, and create reports.

You have built the simple bar chart and mastered the art of layering multiple plot statements to create complex graphs like the Survival Plot using the SGPLOT procedure. You know all about how to use plot statements creatively to get what you need and how to customize the axes to achieve the look and feel you want. Now it s time to up your game and step into the realm of the Graphics Wizard. Behold the magical powers of Graph Template Language Layouts! Here you will learn the esoteric art of creating complex multi-cell graphs using LAYOUT LATTICE. This is the incantation that gives you the power to build complex, multi-cell graphs like the Forest plot, Stock plots with multiple indicators like MACD and Stochastics, Adverse Events by Relative Risk graphs, and more. If you ever wondered how the Diagnostics panel in the REG procedure was built, this paper is for you. Be warned, this is not the realm for the faint of heart!

The new SAS^® Studio application is a web-based interface that provides point-and-click methods that enable you to access a set of commonly used analytical tasks without having to install SAS^® on your local machine. This paper shows how you can use the analytical tasks to explore your data, build a model, and analyze the results' right in your web browser on any Windows, Mac, or mobile device. No SAS programming experience is required to run these tasks, but this application displays the automatically generated SAS procedure code for users who are interested in learning and understanding SAS procedure syntax.

When deploying SAS^® code into a production environment, a programmer should ensure that the code satisfies the following key criteria: The code runs without errors. The code performs operations consistent with the agreed upon business logic. The code is not dependent on manual human intervention. The code performs necessary checks in order to provide sufficient quality control of the deployment process. Base SAS^® programming offers a wide range of techniques to support the last two aforementioned criteria. This presentation demonstrates the use of SAS^® macro variables in combination with simple macro programs to perform a number of routine automated tasks that are often part of the production-ready code. Some of the examples to be demonstrated include the following topics: How to check that required key parameters for a successful program run are populated in the parameters file. How to automatically copy the content of the permanent folder to the newly created backup folder. How to automatically update the log file with new run information. How to check whether a data set already exists in the library.

Everyone has heard about SAS^® Cloud. Now come learn how you can build and manage your own cloud using the same SAS^® virtual application (vApp) technology.

Epidemic modeling is an increasingly important tool in the study of infectious diseases. As technology advances and more and more parameters and data are incorporated into models, it is easy for programs to get bogged down and become unacceptably slow. The use of arrays for importing real data and collecting generated model results in SAS^® can help to streamline the process so results can be obtained and analyzed more efficiently. This paper describes a stochastic mathematical model for transmission of influenza among residents and healthcare workers in long-term care facilities (LTCFs) in New Mexico. The purpose of the model was to determine to what extent herd immunity among LTCF residents could be induced by varying the vaccine coverage among LTCF healthcare workers. Using arrays in SAS made it possible to efficiently incorporate real surveillance data into the model while also simplifying analyses of the results, which ultimately held important implications for LTCF policy and practice.

This session demonstrates how to use Base SAS^® tools to add functional, reusable extensions to the SAS^® system. Learn how to do the following: Write user-defined macro functions that can be used inline with any other SAS code. Use PROC FCMP to write and store user-defined functions that can be used in other SAS programs. Write DS2 user-defined methods and store them in packages for easy reuse in subsequent DS2 programs.

Are you a Java programmer who has been asked to work with SAS^®, or a SAS programmer who has been asked to provide an interface to your IT colleagues? Let s face it, not a lot of Java programmers are heavy SAS users. If this is the case in your company, then you are in luck because SAS provides a couple of really slick features to allow Java programmers to access both SAS data and SAS programming from within a Java program. This paper walks beginner Java or SAS programmers through the simple task of accessing SASdata and SAS programs from a Java program. All that you need is a Java environment and access to a running SAS process, such as a SAS server. This SAS server can either be a SAS/SHARE^® server or an IOM server. However, if you do not have either of these two servers that is okay; with the tools that are provided by SAS, you can start up a remote SAS session within Java and harness the power of SAS.

Have you found OS file permissions to be insufficient to tailor access controls to meet your SAS^® data security requirements? Have you found metadata permissions on tables useful for restricting access to SAS data, but then discovered that SAS programmers can avoid the permissions by issuing LIBNAME statements that do not use the metadata? Would you like to ensure that users have access to only particular rows or columns in SAS data sets, no matter how they access the SAS data sets? Metadata-bound libraries provide the ability to authorize access to SAS data by authenticated Metadata User and Group identities that cannot be bypassed by SAS programmers who attempt to avoid the metadata with direct LIBNAME statements. They also provide the ability to limit the rows and columns in SAS data sets that an authenticated user is allowed to see. The authorization decision is made in the bowels of the SAS^® I/O system, where it cannot be avoided when data is accessed. Metadata-bound libraries were first implemented in the second maintenance release of SAS^® 9.3 and were enhanced in SAS^® 9.4. This paper overviews the feature and discusses best practices for administering libraries bound to metadata and user experiences with bound data. It also discusses enhancements included in the first maintenance release of SAS 9.4.

SAS^® has a wide variety of functions and call routines available. More and more operating system-level functionality has become available as part of SAS language and functions over the versions of SAS. However, there is a wealth of other operating system functionality that can be accessed from within SAS with some preparation on the part of the SAS programmer. Much of the Microsoft Windows functionality is stored in easily re-usable system DLL (Dynamic Link Library) files. This paper describes some of the Windows functionality that might not be available directly as part of SAS language. It also describes methods of accessing that functionality from within SAS code. Using the methods described here, practically any Windows API should become accessible. User-created DLL functionality should also be accessible to SAS programs.

Regression is a helpful statistical tool for showing relationships between two or more variables. However, many users can find the barrage of numbers at best unhelpful, and at worst undecipherable. Using the shipments and inventories historical data from the U.S. Census Bureau's office of Manufacturers' Shipments, Inventories, and Orders (M3), we can create a graphical representation of two time series with PROC GPLOT and map out reported and expected results. By combining this output with results from PROC REG, we are able to highlight problem areas that might need a second look. The resulting graph shows which dates have abnormal relationships between our two variables and presents the data in an easy-to-use format that even users unfamiliar with SAS^® can interpret. This graph is ideal for analysts finding problematic areas such as outliers and trend-breakers or for managers to quickly discern complications and the effect they have on overall results.

The new Markov chain Monte Carlo (MCMC) procedure introduced in SAS/STAT^® 9.2 and further exploited in SAS/STAT^® 9.3 enables Bayesian computations to run efficiently with SAS^®. The MCMC procedure allows one to carry out complex statistical modeling within Bayesian frameworks under a wide spectrum of scientific research; in psychometrics, for example, the estimation of item and ability parameters is a kind. This paper describes how to use PROC MCMC for Bayesian inferences of item and ability parameters under a variety of popular item response models. This paper also covers how the results from SAS PROC MCMC are different from or similar to the results from WinBUGS. For those who are interested in the Bayesian approach to item response modeling, it is exciting and beneficial to shift to SAS, based on its flexibility of data managements and its power of data analysis. Using the resulting item parameter estimates, one can continue to test form constructions, test equatings, etc., with all these test development processes being accomplished with SAS!

Existing health literacy assessment tools developed for research purposes have constraints that limit their utility for clinical practice. The measurement of health literacy in clinical practice can be impractical due to the time requirements of existing assessment tools. Single Item Literacy Screener (SILS) items, which are self-administered brief screening questions, have been developed to address this constraint. We developed a model to predict limited health literacy that consists of two SILS and demographic information (for example, age, race, and education status) using a sample of patients in a St. Louis emergency department. In this paper, we validate this prediction model in a separate sample of patients visiting a primary care clinic in St. Louis. Using the prediction model developed in the previous study, we use SAS/STAT^® software to validate this model based on three goodness of fit criteria: rescaled R-squared, AIC, and BIC. We compare models using two different measures of health literacy, Newest Vital Sign (NVS) and Rapid Assessment of Health Literacy in Medicine Revised (REALM-R). We evaluate the prediction model by examining the concordance, area under the ROC curve, sensitivity, specificity, kappa, and gamma statistics. Preliminary results show 69% concordance when comparing the model results to the REALM-R and 66% concordance when comparing to the NVS. Our conclusion is that validating a prediction model for inadequate health literacy would provide a feasible way to assess health literacy in fast-paced clinical settings. This would allow us to reach patients with limited health literacy with educational interventions and better meet their information needs.

There are yearly 2.35 million road accident cases recorded in the U.S. Among them, 37,000 were considered fatal. Road crashes cost USD 230.6 billion per year, or an average of USD 820 per person. Our efforts are to identify the important factors that lead to vehicle collisions and to predict the injury risk involved in them. Data was collected from National Automotive Sampling System (NASS), containing 20,247 cases with 19 variables. Input variables describe the factors involved in an accident like Height, Age, Weight, Gender, Vehicle model year, Speed limit, Energy absorption in Collision & Deformation location, etc. The target variable is nominal showing levels of injury. Missing values in interval variables were imputed using mean and class variables using the count method. Multivariate analysis suggests high correlation between tire footprint and wheelbase (Corr=0.97, P<0.0001) and original weight of car and curb weight of car (Corr=0.79, P<0.0001). Variables having high kurtosis values were transformed using range standardization. Variables were sorted using variable importance using decision tree analysis. Models like multiple regression, polynomial regression, neural network, and decision tree were applied in the dataset to identify the factors that are most significant in predicting the injury risk. Multilinear perception neural network came out to be the best model to predict injury risk index, with the least Average Squared Error 0.086 in validation dataset.

Developing a good graph with ODS statistical graphics becomes a challenge when the input data maps to crowded displays with overlapping points or lines. Such is the case with the Framingham Heart Study of 5209 subjects captured in the Sashelp.Heart data set, a series of 100 booking curves for the airline industry, and three interleaving series plots that capture closing stock values over a twenty year period for three giants in the computer industry. In this paper, transparency, layering, data point rounding, and color coding are evaluated for their effectiveness to add visual clarity to graphics output. SAS^® Graph Template Language plotting statements (compatible with SAS^® 9.2) that are referenced in this paper include HISTOGRAM, SCATTERPLOT, BANDPLOT, and SERIESPLOT, as well as the layout statements OVERLAY, DATAPANEL, LATTICE, and GRIDDED, which produce single or multiple-panel graphs. SAS Graph Template Language is chosen over ODS Graphics procedures because of its greater graphics capability. While the original version of the paper used SAS 9.2, the latest version incorporates SAS^® 9.3 updates such as HEATMAPPARM for heat maps that add a third dimension to a graph via color, and the RANGEATTRMAP statement for grouping continuous data in a legend. If you have a license for SAS 9.3, you automatically have access to Graph Template Language. Since this is not a tutorial, you will get more out of this presentation if you have read introductory papers or Warren Kuhfeld s book Statistical Graphics in SAS^®: An Introduction to the Graph Template Language and the Statistical Graphics Procedures .

Dataprev has become the principal owner of social data on the citizens in Brazil by collecting information for over forty years in order to subsidize pension applications for the government. The use of this data can be expanded to provide new tools to aid policy and assist the government to optimize the use of its resources. Using SAS^® MDM, we are developing a solution that uniquely identifies the citizens of Brazil. Overcoming challenges with multiple government agencies and with the validation of survey records that suggest the same person requires rules for governance and a definition of what represents a particular Brazilian citizen. In short, how do you turn a repository of master data into an efficient catalyst for public policy? This is the goal for creating a repository focused on identifying the citizens of Brazil.

This presentation will teach the audience how to use SAS^® ODS Graphics. Now part of Base SAS^®, ODS Graphics is a great way to easily create clear graphics that enable any user to tell their story well. SGPLOT and SGPANEL are two of the procedures that can be used to produce powerful graphics that used to require a lot of work. The core of the procedures are explained, as well as the options available. Furthermore, we explore the ways to combine the individual statements to make more complex graphics that tell the story better. Any user of Base SAS on any platform will find great value from the SAS ODS Graphics procedures.

This paper discusses the techniques I used at the Census Bureau to overcome the issue of dealing with large amounts of data while modernizing some of their public-facing web applications by using service oriented architecture (SOA) to deploy Flex web applications powered by SAS^®. The paper covers techniques that resulted in reducing 142,293 XML lines (3.6 MB) down to 15,813 XML lines (1.8 MB), a 50% size reduction on the server side (HTTP Response), and 196,167 observations down to 283 observations, a reduction of 99.8% in summarized data on the client side (XML Lookup file).

In the past, calibration was done by using extremely complicated macros in Base SAS^® to create a Microsoft Excel workbook with multiple linked spreadsheets. This process made it hard to audit, was not reliably replicable, and was open to user error. The task was to create a replicable, auditable, and locked down application that allowed the user to change certain parameters and see the impact of those changes without needing to code. SAS^® Stored Processes are used to generate a screen that is split into three sections: one shows static reporting, the second is a data-driven custom input form, and the third shows test results. The initial screen uses a standard stored process that enables the user to select the model and time period. Macro variables are passed through to subset data. The Static reports are created from a stored process that executes two REPORT procedures that subset the data based on the passed parameters. The form is built using SAS^® to generate HTML and is data driven. The Update button at the end of the form executes a stored process that collects the data that the user has entered into the form and updates a database. After the rates have been updated, they are used to generate test results using PROC REPORT.

The Affordable Care Act that is being implemented now is expected to fundamentally reshape the health care industry. All current participants--providers, subscribers, and payers--will operate differently under a new set of key performance indicators (KPIs). This paper uses public data and SAS^® software to establish a baseline for the health care industry today so that structural changes can be measured in the future to establish the impact of the new laws.

Health plans use wide-ranging interventions based on criteria set by nationally recognized organizations (for example, NCQA and CMS) to change health-related behavior in large populations. Evaluation of these interventions has become more important with the increased need to report patient-centered quality of care outcomes. Findings from evaluations can detect successful intervention elements and identify at-risk patients for further targeted interventions. This paper describes how SAS^® was applied to evaluate the effectiveness of a patient-directed intervention designed to increase medication adherence and a health plan s CMS Part D Star Ratings. Topics covered include querying data warehouse tables, merging pharmacy and eligibility claims, manipulating data to create outcome variables, and running statistical tests to measure pre-post intervention differences.

Comprehensive cancer centers have been mandated to engage communities in their work; thus, measurement of community engagement is a priority area. Siteman Cancer Center s Program for the Elimination of Cancer Disparities (PECaD) projects seek to align with 11 Engagement Principles (EP) previously developed in the literature. Participants in a PECaD pilot project were administered a survey with questions on community engagement in order to evaluate how well the project aligns with the EPs. Internal consistency is examined using PROC CORR with the ALPHA option to calculate Cronbach s alpha for questions that relate to the same EP. This allows items that have a lack of internal consistency to be identified and to be edited or removed from the assessment. EP-specific scores are developed on quantity and quality scales. Lack of internal consistency was found for six of the 16 EP s examined items (alpha<.70). After editing the items, all EP question groups had strong internal consistency (alpha>.85). There was a significant positive correlation between quantity and quality scores (r=.918, P<.001). Average EP-specific scores ranged from 6.87 to 8.06; this suggests researchers adhered to the 11 EPs between sometime and most of the time on the quantity scale and between good and very good on the quality scale. Examining internal consistency is necessary to develop measures that accurately determine how well PECaD projects align with EPs. Using SAS^® to determine internal consistency is an integral step in the development of community engagement scores.

Especially in this current financial climate, many of us are being asked to do more with less. For several years, the Office of Institutional Research and Testing at Baylor University has been using SAS^® software to increase the efficiency of the office and of the University as a whole. Reports that were once prepared manually have been automated. Data quality processes have been implemented in order to reduce the number of duplicate mailings. Predictive modeling is used to focus recruiting efforts on those prospective students most likely to respond. A web-based portal has been created to provide self-service report generation for many administrators across campus. Along with this, a number of data processing functions have been centralized, eliminating the need for additional programming skills and software support. This presentation discusses these improvements in more detail and provides examples of the end results.

The Patient-Centered Outcomes Research Institute (PCORI) was created as part of the Affordable Care Act. PCORI is authorized by Congress to conduct research to provide information about the best available evidence to help patients and their health care providers make more informed decisions. Community Care Behavioral Health Organization in Pittsburgh, Pennsylvania was awarded a PCORI research grant to investigate health care system improvements for adults with serious mental illness. The grant, titled Optimizing Behavioral Health Homes by Focusing on Outcomes that Matter Most for Adults with Serious Mental Illness, began in January of 2013 and is ongoing. Information Technology staff at Community Care have leveraged SAS^® solutions in providing real-time data extraction and reports to support the development and implementation of this research project. SAS tools have been used to merge data from multiple platforms and database sources, including web data sources. SAS has also enabled the formatting and traffic lighting of multiple Microsoft Excel data sets and files, in addition to the creation of many operational reports and data files needed for study implementation, administration, and maintenance. The challenges faced and the SAS solutions employed are the subject of this paper.

When providing lengthy cost and utilization data to medical providers, it is ideal to sort the report by descending cost (or utilization) so that the important categories are at the top. This task can be easily solved using PROC SORT. However, when you need other variables (such as unit cost per procedure or national average) to follow the sort but not be sorted themselves, the solution is not as intuitive. This paper looks at several sorting algorithms to solve this problem. First, we look at the basic bubble sort (which is still effective for smaller data sets), which sets up arrays for each variable and then sorts on just one of them. Next, we discuss the quicksort algorithm, which is effective for large data sets, too. The results of the sorts provide sorted data that is easy to read and makes for effective analysis.

When reading data files or writing SAS^® programs, we are often hunting for the right format or informat. There are so many to choose from! Does it seem like too many to search the manual? Let SAS help find the right one! We use the SAS dictionary table VFORMAT and a very small SAS program. This presentation demonstrates how two simple functions unlock the potential of this great resource: SASHELP.VFORMAT.

Researchers often rely on self-report for survey based studies. The accuracy of this self-reported data is often unknown, particularly in a medical setting that serves an under-insured patient population with varying levels of health literacy. We recruited participants from the waiting room of a St. Louis primary care safety net clinic to participate in a survey investigating the relationship between health environments and health outcomes. The survey included questions regarding personal and family history of chronic disease (diabetes, heart disease, and cancer) as well as BMI and self-perceived weight. We subsequently accessed the participant s electronic medical record (EMR) and collected physician-reported data on the same variables. We calculated concordance rates between participant answers and information gathered from EMRs using McNemar s chi-squared test. Logistic regression was then performed to determine the demographic predictors of concordance. Three hundred thirty-two patients completed surveys as part of the pilot phase of the study; 64% female, 58% African American, 4% Hispanic, 15% with less than high school level education, 76% annual household income less than $20,000, and 29% uninsured. Preliminary findings suggest an 82-94% concordance rate between self-reported and medical record data across outcomes, with the exception of family history of cancer (75%) and heart disease (42%). Our conclusion is that determining the validity of the self-reported data in the pilot phase influences whether self-reported personal and family history of disease and BMI are appropriate for use in this patient population.

The demand for scalable and approachable analytics through easy-to-use interfaces has increased exponentially. SAS has developed new web-based analytic interfaces that extend the capabilities of its data mining and forecasting web suites to address this demand. By taking advantage of our latest high-performance analytics technology, SAS users can build scalable models with an automated approach. Why build one model when you can use new clients from SAS to build hundreds--incorporating all of your data--with a few clicks? With a model factory approach, users can build models down to the product and SKU level, and SAS will produce exception-based reports to aid adjustments. During this session, you will gain an early glimpse into the latest analytic web interface development and have an opportunity to provide feedback.

The world's first wind resource assessment buoy, residing in Lake Michigan, uses a pulsing laser wind sensor to accurately measure wind speed, direction, and turbulence offshore up to wind turbine hub-height and across the blade span every second. Understanding wind behavior would be tedious and fatiguing with such large data sets. However, SAS/GRAPH^® 9.4 helps the user grasp wind characteristics over time and at different altitudes by exploring the data visually. This paper covers graphical approaches to evaluate wind speed validity, seasonal wind speed variation, and storm systems to inform engineers on the candidacy of Lake Michigan offshore wind farms.

Volatility estimation plays an important role in the elds of statistics and nance. Many different techniques address the problem of estimating volatility of nancial assets. Autoregressive conditional heteroscedasticity (ARCH) models and the related generalized ARCH models are popular models for volatility. This talk will introduce the need for volatility modeling as well as introduce the framework of ARCH and GARCH models. A brief discussion about the structure of ARCH and GARCH models will then be compared to other volatility modeling techniques.

Data quality is at the very heart of accurate, relevant, and trusted information, but traditional techniques that require the data to be moved, cleansed, and repopulated simply can't scale up to cover the ultra-jumbo nature of big data environments. This paper describes how SAS^® Data Quality accelerators for databases like Teradata and Hadoop deliver data quality for big data by operating in situ and in parallel on each of the nodes of these clustered environments. The paper shows how data quality operations can be easily modified to leverage these technologies. It examines the results of performance benchmarks that show how in-database operations can scale to meet the demands of any use case, no matter how big a big data mammoth you have.

Missing observations caused by dropouts or skipped visits present a problem in studies of longitudinal data. When the analysis is restricted to complete cases and the missing data depend on previous responses, the generalized estimating equation (GEE) approach, which is commonly used when the population-average effect is of primary interest, can lead to biased parameter estimates. The new GEE procedure in SAS/STAT^® 13.2 implements a weighted GEE method, which provides consistent parameter estimates when the dropout mechanism is correctly specified. When none of the data are missing, the method is identical to the usual GEE approach, which is available in the GENMOD procedure. This paper reviews the concepts and statistical methods. Examples illustrate how you can apply the GEE procedure to incomplete longitudinal data.
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In connection with the consolidation work at Nykredit, the data stored on the Nykredit z/OS SAS^® installation had to be migrated (copied) to the new x64 Windows SAS platform storage. However, getting an overview of these data on the z/OS mainframe can be difficult, and a series of questions arise during the process. For example: Who is responsible? How many bytes? How many rows and columns? When were the data created? And so on. With extensive use of filename FTP and looping, and extracting metadata, it is possible to get an overview of the data on the host presented in a Microsoft Excel spreadsheet.

A Norwegian hospital, Nordlandssykehuset, is using SAS^® to automate the Global Trigger Tool (GTT) method to monitor and reveal incidents of adverse events in the treatment of patients by search of structured and unstructured data within medical records.

Do you know everything you need to know about missing values? Do you know how to assign a missing value to multiple variables with one statement? Can you display missing values as something other than . or blank? How many types of missing numeric values are there? This paper reviews techniques for assigning, displaying, referencing, and summarizing missing values for numeric variables and character variables.

The latest releases of SAS^® Data Integration Studio and SAS^® Data Management provide an integrated environment for managing and transforming your data to meet new and increasingly complex data management challenges. The enhancements help develop efficient processes that can clean, standardize, transform, master, and manage your data. The latest features include: capabilities for building complex job processes web and tablet environments for managing your data enhanced ELT transformation capabilities big data transformation capabilities for Hadoop integration with the SAS^® LASR™ platform enhanced features for lineage tracing and impact analysis new features for master data and metadata management This paper provides an overview of the latest features of the products and includes use cases and examples for leveraging product capabilities.

Over the last year, the SAS^® Enterprise Miner^™ development team has made numerous and wide-ranging enhancements and improvements. New utility nodes that save data, integrate better with open-source software, and register models make your routine tasks easier. The area of time series data mining has three new nodes. There are also new models for Bayesian network classifiers, generalized linear models (GLMs), support vector machines (SVMs), and more.

SAS^® Merchandise Planning introduces key changes with the recent 6.4 release and the upcoming 6.5 release. This session highlights the integration to SAS^® Visual Analytics, the analytic infrastructure that enables users to integrate analytic results into their planning decisions, as well as multiple usability enhancements. Included is a look at the first of the packaged analytics that include the Recommended Assortment analytic.

Expensive physical capital must be regularly maintained for optimal efficiency and long-term insurance against damage. The maintenance process usually consists of constantly monitoring high-frequency sensor data and performing corrective maintenance when the expected values do not match the actual values. An economic system can also be thought of as a system that requires constant monitoring and occasional maintenance in the form of monetary or fiscal policy. This paper shows how to use the SSM procedure in SAS/ETS^® to make forecasts of expected values by using high-frequency multivariate time series. The paper also demonstrates the functionality of the new SASEFRED interface engine in SAS/ETS.

SAS^® has an amazing arsenal of tools to use and display geographic information that is relatively unknown and underutilized. This presentation will highlight both new and existing capacities for creating stunning, informative maps as well as using geographic data in other ways. SAS provided map data files, functions, format libraries and other geographic data files will be explored in detail. Custom mapping of geographic areas will be discussed. Maps produced will include use of both the annotate facility (including some new functions) and PROC GREPLAY. Products used are Base SAS^® and SAS/GRAPH^®. SAS programmers of any skill level will benefit from this presentation.

A European utility company has several thousand service engineers who provide its customers with services that range from performing routine maintenance to handling emergency breakdowns. Each service engineer is assigned to a work area that consists of a set of postal sectors. The company wants to understand how it should configure its work areas to improve customer satisfaction, minimize travel time for its full-time service engineers, and minimize the costs of overtime and subcontractor hours. This paper describes the use of SAS/OR^® optimization procedures to model this problem and configure optimal work areas, and the use of SAS^® Simulation Studio to simulate how the optimal configurations might satisfy the customer service requirements. The experimental results show that the proposed solution can satisfy customer demand within the desired service-time window, with significantly less travel time for the engineers, and with lower overtime and subcontractor costs.

The DATA step allows one to read, write, and manipulate many types of data. As data evolves to a more free-form state, the ability of SAS^® to handle character data becomes increasingly important. This paper addresses character data from multiple vantage points. For example, what is the default length of a character string, and why does it appear to change under different circumstances? What type of formatting is available for character data? How can we examine and manipulate character data? The audience for this paper is beginner to intermediate, and the goal is to provide an introduction to the numerous character functions available in SAS, including the basic LENGTH and SUBSTR functions, plus many others.

SAS^® Visual Analytics is one of the newer SAS^® products with a lot of excitement surrounding it. But what is SAS Visual Analytics really? By examining the similarities, differences, and synergies between SAS Visual Analytics and other SAS offerings, we can more clearly understand this new product.

We receive a daily file with information about patients who use our drug. It s updated every day so that we have the most current information. Nearly every variable on a patient s record can be different from one day to the next. But what if you wanted to capture information that changed? For example, what if a patient switched doctors sometime along the way, and the original prescribing doctor is different than the patient's present doctor? With this type of daily file, that information is lost. To avoid losing these changes, you have to build a cumulative data set. I ll show you how to build it.

57 Category teams. 8,500 stores. 10,000 SKUs. 1 integrated Planning Solution. Deploying a stand-alone Assortment Planning system creates an isolated planning structure that adds complexity to your ability to deliver results in a dynamic retail environment. Today s challenging competitive and economic conditions reward retailers who take the opportunity to integrate their strategic systems into their downstream execution. This presentation describes the approach Family Dollar followed to integrate SAS^® Assortment Planning with existing operational systems. The result? Reduced complexity, improved efficiency, and better on-time execution in our stores.