Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, might result from repeatedly measuring the outcome, for various members of the same family, and so on. The first issue is dealt with through a variety of overdispersion models such as the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena might occur simultaneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary, count, and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic-numerical integration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study of epileptic seizures, a clinical trial for a toenail infection named onychomycosis, and survival data in children with asthma.

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With increasing regulatory emphasis on using more scientific statistical processes and procedures in the Bank Secrecy Act/Anti-Money Laundering (BSA/AML) compliance space, financial institutions are being pressured to replace their heuristic, rule-based customer risk rating models with well-established, academically supported, statistically based models. As part of their customer-enhanced due diligence, firms are expected to both rate and monitor every customer for the overall risk that the customer poses. Firms with ineffective customer risk rating models can face regulatory enforcement actions such as matters requiring attention (MRAs); the Office of the Comptroller of the Currency (OCC) can issue consent orders for federally chartered banks; and the Federal Deposit Insurance Corporation (FDIC) can take similar actions against state-chartered banks. Although there is a reasonable amount of information available that discusses the use of statistically based models and adherence to the OCC bulletin Supervisory Guidance on Model Risk Management (OCC 2011-12), there is only limited material about the specific statistical techniques that financial institutions can use to rate customer risk. This paper discusses some of these techniques; compares heuristic, rule-based models and statistically based models; and suggests ordinal logistic regression as an effective statistical modeling technique for assessing customer BSA/AML compliance risk. In discussing the ordinal logistic regression model, the paper addresses data quality and the selection of customer risk attributes, as well as the importance of following the OCC's key concepts for developing and managing an effective model risk management framework. Many statistical models can be used to assign customer risk, but logistic regression, and in this case ordinal logistic regression, is a fairly common and robust statistical method of assigning customers to ordered classifications (such as Low, Medium, High-Low, High-Medium, and High-High risk). Using ordinal logistic regression, a financial institution can create a customer risk rating model that is effective in assigning risk, justifiable to regulators, and relatively easy to update, validate, and maintain.

A complex survey data set is one characterized by any combination of the following four features: stratification, clustering, unequal weights, or finite population correction factors. In this paper, we provide context for why these features might appear in data sets produced from surveys, highlight some of the formulaic modifications they introduce, and outline the syntax needed to properly account for them. Specifically, we explain why you should use the SURVEY family of SAS/STAT^® procedures, such as PROC SURVEYMEANS or PROC SURVEYREG, to analyze data of this type. Although many of the syntax examples are drawn from a fictitious expenditure survey, we also discuss the origins of complex survey features in three real-world survey efforts sponsored by statistical agencies of the United States government--namely, the National Ambulatory Medical Care Survey, the National Survey of Family of Growth, and the Consumer Building Energy Consumption Survey.

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The importance of econometrics in the analytics toolkit is increasing every day. Econometric modeling helps uncover structural relationships in observational data. This paper highlights the many recent changes to the SAS/ETS^® portfolio that increase your power to explain the past and predict the future. Examples show how you can use Bayesian regression tools for price elasticity modeling, use state space models to gain insight from inconsistent time series, use panel data methods to help control for unobserved confounding effects, and much more.

In many spatial analysis applications (including crime analysis, epidemiology, ecology, and forestry), spatial point process modeling can help you study the interaction between different events and help you model the process intensity (the rate of event occurrence per unit area). For example, crime analysts might want to estimate where crimes are likely to occur in a city and whether they are associated with locations of public features such as bars and bus stops. Forestry researchers might want to estimate where trees grow best and test for association with covariates such as elevation and gradient. This paper describes the SPP procedure, new in SAS/STAT^® 13.2, for exploring and modeling spatial point pattern data. It describes methods that PROC SPP implements for exploratory analysis of spatial point patterns and for log-linear intensity modeling that uses covariates. It also shows you how to use specialized functions for studying interactions between points and how to use specialized analytical graphics to diagnose log-linear models of spatial intensity. Crime analysis, forestry, and ecology examples demonstrate key features of PROC SPP.

Managing the large-scale displacement of people and communities caused by a natural disaster has historically been reactive rather than proactive. Following a disaster, data is collected to inform and prompt operational responses. In many countries prone to frequent natural disasters such as the Philippines, large amounts of longitudinal data are collected and available to apply to new disaster scenarios. However, because of the nature of natural disasters, it is difficult to analyze all of the data until long after the emergency has passed. For this reason, little research and analysis have been conducted to derive deeper analytical insight for proactive responses. This paper demonstrates the application of SAS^® analytics to this data and establishes predictive alternatives that can improve conventional storm responses. Humanitarian organizations can use this data to understand displacement patterns and trends and to optimize evacuation routing and planning. Identifying the main contributing factors and leading indicators for the displacement of communities in a timely and efficient manner prevents detrimental incidents at disaster evacuation sites. Using quantitative and qualitative methods, responding organizations can make data-driven decisions that innovate and improve approaches to managing disaster response on a global basis. The benefits of creating a data-driven analytical model can help reduce response time, improve the health and safety of displaced individuals, and optimize scarce resources in a more effective manner. The International Organization for Migration (IOM), an intergovernmental organization, is one of the first-response organizations on the ground that responds to most emergencies. IOM is the global co-load for the Camp Coordination and Camp Management (CCCM) cluster in natural disasters. This paper shows how to use SAS^® Visual Analytics and SAS^® Visual Statistics for the Philippines in response to Super Typhoon Haiyan in Nove mber 2013 to develop increasingly accurate models for better emergency-preparedness. Using data collected from IOM's Displacement Tracking Matrix (DTM), the final analysis shows how to better coordinate service delivery to evacuation centers sheltering large numbers of displaced individuals, applying accurate hindsight to develop foresight on how to better respond to emergencies and disasters. Predictive models build on patterns found in historical and transactional data to identify risks and opportunities. The capacity to predict trends and behavior patterns related to displacement and mobility has the potential to enable the IOM to respond in a more timely and targeted manner. By predicting the locations of displacement, numbers of persons displaced, number of vulnerable groups, and sites at most risk of security incidents, humanitarians can respond quickly and more effectively with the appropriate resources (material and human) from the outset. The end analysis uses the SAS^® Storm Optimization model combined with human mobility algorithms to predict population movement.

The use of administrative databases for understanding practice patterns in the real world has become increasingly apparent. This is essential in the current health-care environment. The Affordable Care Act has helped us to better understand the current use of technology and different approaches to surgery. This paper describes a method for extracting specific information about surgical procedures from the Healthcare Cost and Utilization Project (HCUP) database (also referred to as the National (Nationwide) Inpatient Sample (NIS)).The analyses provide a framework for comparing the different modalities of surgerical procedures of interest. Using an NIS database for a single year, we want to identify cohorts based on surgical approach. We do this by identifying the ICD-9 codes specific to robotic surgery, laparoscopic surgery, and open surgery. After we identify the appropriate codes using an ARRAY statement, a similar array is created based on the ICD-9 codes. Any minimally invasive procedure (robotic or laparoscopic) that results in a conversion is flagged as a conversion. Comorbidities are identified by ICD-9 codes representing the severity of each subject and merged with the NIS inpatient core file. Using a FORMAT statement for all diagnosis variables, we create macros that can be regenerated for each type of complication. These created macros are compiled in SAS^® and stored in the library that contains the four macros that are called by tables. They call the macros for different macros variables. In addition, they create the frequencies of all cohorts and create the table structure with the title and number of the table. This paper describes a systematic method in SAS/STAT^® 9.2 to extract the data from NIS using the ARRAY statement for the specific ICD-9 codes, to format the extracted data for the analysis, to merge the different NIS databases by procedures, and to use automatic macros to generate the report.

Census data, such as education and income, has been extensively used for various purposes. The data is usually collected in percentages of census unit levels, based on the population sample. Such presentation of the data makes it hard to interpret and compare. A more convenient way of presenting the data is to use the geocoded percentage to produce counts for a pseudo-population. We developed a very flexible SAS^® macro to automatically generate the descriptive summary tables for the census data as well as to conduct statistical tests to compare the different levels of the variable by groups. The SAS macro is not only useful for census data but can be used to generate summary tables for any data with percentages in multiple categories.

In observational data analyses, it is often helpful to use patients as their own controls by comparing their outcomes before and after some signal event, such as the initiation of a new therapy. It might be useful to have a control group that does not have the event but that is instead evaluated before and after some arbitrary point in time, such as their birthday. In this context, the change over time is a continuous outcome that can be modeled as a (possibly discontinuous) line, with the same or different slope before and after the event. Mixed models can be used to estimate random slopes and intercepts and compare patients between groups. A specific example published in a peer-reviewed journal is presented.

Kaiser Permanente Northwest is contractually obligated for regulatory submissions to Oregon Health Authority, Health Share of Oregon, and Molina Healthcare in Washington. The submissions consist of Medicaid Encounter data for medical and pharmacy claims. SAS^® programs are used to extract claims data from Kaiser's claims data warehouse, process the data, and produce output files in HIPAA ASC X12 and NCPDP format. Prior to April 2014, programs were written in SAS^® 8.2 running on a VAX server. Several key drivers resulted in the conversion of the existing system to SAS^® Enterprise Guide^® 5.1 running on UNIX. These drivers were: the need to have a scalable system in preparation for the Affordable Care Act (ACA); performance issues with the existing system; incomplete process reporting and notification to business owners; and a highly manual, labor-intensive process of running individual programs. The upgraded system addressed these drivers. The estimated cost reduction was from $1.30 per reported encounter to $0.13 per encounter. The converted system provides for better preparedness for the ACA. One expected result of ACA is significant Medicaid membership growth. The program has already increased in size by 50% in the preceding 12 months. The updated system allows for the expected growth in membership.

Dashboards are an effective tool for analyzing and summarizing the large volumes of data required to manage loan portfolios. Effective dashboards must highlight the most critical drivers of risk and performance within the portfolios and must be easy to use and implement. Developing dashboards often require integrating data, analysis, or tools from different software platforms into a single, easy-to-use environment. FI Consulting has developed a Credit Modeling Dashboard in Microsoft Access that integrates complex models based on SAS into an easy-to-use, point-and-click interface. The dashboard integrates, prepares, and executes back-end models based on SAS using command-line programming in Microsoft Access with Visual Basic for Applications (VBA). The Credit Modeling Dashboard developed by FI Consulting represents a simple and effective way to supply critical business intelligence in an integrated, easy-to-use platform without requiring investment in new software or to rebuild existing SAS tools already in use.

The success of an experimental study almost always hinges on how you design it. Does it provide estimates for everything you're interested in? Does it take all the experimental constraints into account? Does it make efficient use of limited resources? The OPTEX procedure in SAS/QC^® software enables you to focus on specifying your interests and constraints, and it takes responsibility for handling them efficiently. With PROC OPTEX, you skip the step of rifling through tables of standard designs to try to find the one that's right for you. You concentrate on the science and the analytics and let SAS^® do the computing. This paper reviews the features of PROC OPTEX and shows them in action using examples from field trials and food science experimentation. PROC OPTEX is a useful tool for all these situations, doing the designing and freeing the scientist to think about the food and the biology.

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Competing risk arise in time to event data when the event of interest cannot be observed because of a preceding event i.e. a competing event occurring before. An example can be of an event of interest being a specific cause of death where death from any other cause can be termed as a competing event, if focusing on relapse, death before relapse would constitute a competing event. It is well studied and pointed out that in presence of competing risks, the standard product limit methods yield biased results due to violation of their basic assumption. The effect of competing events on parameter estimation depends on their distribution and frequency. Fine and Gray's sub-distribution hazard model can be used in presence of competing events which is available in PROC PHREG with the release of version 9.4 of SAS^® software.

Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are a few forecasting techniques (Croston's method and others). This paper proposes a technique for large-scale automatic count series forecasting and uses SAS^® Forecast Server and SAS/ETS^® software to demonstrate this technique.

Texas is one of about 30 states that has recently passed laws requiring voters to produce valid IDs in an effort to avoid illegal voters. This new regulation, however, might negatively affect voting opportunities for students, low-income people, and minorities. To determine the actual effects of the regulation in Dallas County, voters were surveyed when exiting the polling offices during the November midterm election about difficulties that they might have encountered in the voting process. The database of the voting history of each registered voter in the county was examined, and the data set was converted into an analyzable structure prior to stratification. All of the polling offices were stratified by the residents' degrees of involvement in the past three general elections, namely, the proportion of people who have used early election and who have at least voted once. A two-phase sampling design was adopted for stratification. On election day, pollsters were sent to select polling offices and interviewed 20 voters at a selected time period. The number of people having difficulties was estimated when data was collected.

The purpose of this paper is to introduce a SAS^® macro named %DOUBLEGLM that enables users to model the mean and dispersion jointly using double generalized linear models described in Nelder (1991) and Lee (1998). The R functions FITJOINT and DGLM (R Development Core Team, 2011) were used to verify the suitability of the %DOUBLEGLM macro estimates. The results showed that estimates were closer than the R functions.

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Respondent Driven Sampling (RDS) is both a sampling method and a data analysis technique. As a sampling method, RDS is a chain referral technique with strategic recruitment quotas and specific data gathering requirements. Like other chain referral techniques (for example, snowball sampling), the chains and waves are the start point to conduct analysis. But building the chains and waves still would be a daunting task because it involves too many transpositions and merges. This paper provides an efficient method of using Base SAS^® to build up chains and waves.

While there has been tremendous progress in technologies related to data storage, high-performance computing, and advanced analytic techniques, organizations have only recently begun to comprehend the importance of parallel strategies that help manage the cacophony of concerns around access, quality, provenance, data sharing, and use. While data governance is not new, the drumbeat around it, along with master data management and data quality, is approaching a crescendo. Intensified by the increase in consumption of information, expectations about ubiquitous access, and highly dynamic visualizations, these factors are also circumscribed by security and regulatory constraints. In this paper, we provide a summary of what data governance is and its importance. We go beyond the obvious and provide practical guidance on what it takes to build out a data governance capability appropriate to the scale, size, and purpose of the organization and its culture. Moreover, we discuss best practices in the form of requirements that highlight what we think is important to consider as you provide that tactical linkage between people, policies, and processes to the actual data lifecycle. To that end, our focus includes the organization and its culture, people, processes, policies, and technology. Further, we include discussions of organizational models as well as the role of the data steward, and provide guidance on how to formalize data governance into a sustainable set of practices within your organization.

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With the constant need to inform researchers about neighborhood health data, the Santa Clara County Health Department created socio-demographic and health profiles for 109 neighborhoods in the county. Data was pulled from many public and county data sets, compiled, analyzed, and automated using SAS^®. With over 60 indicators and 109 profiles, an efficient set of macros was used to automate the calculation of percentages, rates, and mean statistics for all of the indicators. Macros were also used to automate individual census tracts into pre-decided neighborhoods to avoid data entry errors. Simple SQL procedures were used to calculate and format percentages within the macros, and output was pushed out using Output Delivery System (ODS) Graphics. This output was exported to Microsoft Excel, which was used to create a sortable database for end users to compare cities and/or neighborhoods. Finally, the automated SAS output was used to map the demographic data using geographic information system (GIS) software at three geographies: city, neighborhood, and census tract. This presentation describes the use of simple macros and SAS procedures to reduce resources and time spent on checking data for quality assurance purposes. It also highlights the simple use of ODS Graphics to export data to an Excel file, which was used to mail merge the data into 109 unique profiles. The presentation is aimed at intermediate SAS users at local and state health departments who might be interested in finding an efficient way to run and present health statistics given limited staff and resources.

Your electricity usage patterns reveal a lot about your family and routines. Information collected from electrical smart meters can be mined to identify patterns of behavior that can in turn be used to help change customer behavior for the purpose of altering system load profiles. Demand Response (DR) programs represent an effective way to cope with rising energy needs and increasing electricity costs. The Federal Energy Regulatory Commission (FERC) defines demand response as changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to lower electricity use at times of high wholesale market prices or when system reliability of jeopardized. In order to effectively motivate customers to voluntarily change their consumptions patterns, it is important to identify customers whose load profiles are similar so that targeted incentives can be directed toward these customers. Hence, it is critical to use tools that can accurately cluster similar time series patterns while providing a means to profile these clusters. In order to solve this problem, though, hardware and software that is capable of storing, extracting, transforming, loading and analyzing large amounts of data must first be in place. Utilities receive customer data from smart meters, which track and store customer energy usage. The data collected is sent to the energy companies every fifteen minutes or hourly. With millions of meters deployed, this quantity of information creates a data deluge for utilities, because each customer generates about three thousand data points monthly, and more than thirty-six billion reads are collected annually for a million customers. The data scientist is the hunter, and DR candidate patterns are the prey in this cat-and-mouse game of finding customers willing to curtail electrical usage for a program benefit. The data scientist must connect large siloed data sources, external data , and even unstructured data to detect common customer electrical usage patterns, build dependency models, and score them against their customer population. Taking advantage of Hadoop's ability to store and process data on commodity hardware with distributed parallel processing is a game changer. With Hadoop, no data set is too large, and SAS^® Visual Statistics leverages machine learning, artificial intelligence, and clustering techniques to build descriptive and predictive models. All data can be usable from disparate systems, including structured, unstructured, and log files. The data scientist can use Hadoop to ingest all available data at rest, and analyze customer usage patterns, system electrical flow data, and external data such as weather. This paper will use Cloudera Hadoop with Apache Hive queries for analysis on platforms such as SAS^® Visual Analytics and SAS Visual Statistics. The paper will showcase optionality within Hadoop for querying large data sets with open-source tools and importing these data into SAS^® for robust customer analytics, clustering customers by usage profiles, propensity to respond to a demand response event, and an electrical system analysis for Demand Response events.

SAS^® University Edition is a great addition to the world of freely available analytic software, and this 'how-to' presentation shows you how to implement a discrete event simulation using Base SAS^® to model future US Veterans population distributions. Features include generating a slideshow using ODS output to PowerPoint.

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The SAS^® Web Application Server is a lightweight server that provides enterprise-class features for running SAS^® middle-tier web applications. This server can be configured to use the SAS^® Web Infrastructure Platform Data Server for a transactional storage database. You can meet the high-availability data requirement in your business plan by implementing a SAS Web Infrastructure Data Server cluster. This paper focuses on how the SAS Web Infrastructure Data Server on the SAS middle tier can be configured for load balancing, and data replication involving multiple nodes. SAS^® Environment Manager and pgpool-II are used to enable these high-availability strategies, monitor the server status, and initiate failover as needed.

Effect modification occurs when the association between a predictor of interest and the outcome is differential across levels of a third variable--the modifier. Effect modification is statistically tested as the interaction effect between the predictor and the modifier. In repeated measures studies (with more than two time points), higher-order (three-way) interactions must be considered to test effect modification by adding time to the interaction terms. Custom fitting and constructing these repeated measures models are difficult and time consuming, especially with respect to estimating post-fitting contrasts. With the advancement of the LSMESTIMATE statement in SAS^®, a simplified approach can be used to custom test for higher-order interactions with post-fitting contrasts within a mixed model framework. This paper provides a simulated example with tips and techniques for using an application of the nonpositional syntax of the LSMESTIMATE statement to test effect modification in repeated measures studies. This approach, which is applicable to exploring modifiers in randomized controlled trials (RCTs), goes beyond the treatment effect on outcome to a more functional understanding of the factors that can enhance, reduce, or change this relationship. Using this technique, we can easily identify differential changes for specific subgroups of individuals or patients that subsequently impact treatment decision making. We provide examples of conventional approaches to higher-order interaction and post-fitting tests using the ESTIMATE statement and compare and contrast this to the nonpositional syntax of the LSMESTIMATE statement. The merits and limitations of this approach are discussed.

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Many scientific and academic journals require that statistical tables be created in a specific format, with one of the most common formats being that of the American Psychological Association (APA). The APA publishes a substantial guide book to writing and formatting papers, including an extensive section on creating tables (Nichol 2010). However, the output generated by SAS^® procedures does not match this style. This paper discusses techniques to change the SAS procedure output to match the APA guidelines using SAS ODS (Output Delivery System).

A common complaint of employers is that educational institutions do not prepare students for the types of messy data and multi-faceted requirements that occur on the job. No organization has data that resembles the perfectly scrubbed data sets in the back of a statistics textbook. The objective of the Annual Report Project is to quickly bring new SAS^® users to a level of competence where they can use real data to meet real business requirements. Many organizations need annual reports for stockholders, funding agencies, or donors. Or, they need annual reports at the department or division level for an internal audience. Being tapped as part of the team creating an annual report used to mean weeks of tedium, poring over columns of numbers in 8-point font in (shudder) Excel spreadsheets, but no more. No longer painful, using a few SAS procedures and functions, reporting can be easy and, dare I say, fun. All analyses are done using SAS^® Studio (formerly SAS^® Web Editor) of SAS OnDemand for Academics. This paper uses an example with actual data for a report prepared to comply with federal grant funding requirements as proof that, yes, it really is that simple.

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Retrospective case-control studies are frequently used to evaluate health care programs when it is not feasible to randomly assign members to a respective cohort. Without randomization, observational studies are more susceptible to selection bias where the characteristics of the enrolled population differ from those of the entire population. When the participant sample is different from the comparison group, the measured outcomes are likely to be biased. Given this issue, this paper discusses how propensity score matching and random effects techniques can be used to reduce the impact selection bias has on observational study outcomes. All results shown are drawn from an ROI analysis using a participant (cases) versus non-participant (controls) observational study design for a fitness reimbursement program aiming to reduce health care expenditures of participating members.

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Replication techniques such as the jackknife and the bootstrap have become increasingly popular in recent years, particularly within the field of complex survey data analysis. The premise of these techniques is to treat the data set as if it were the population and repeatedly sample from it in some systematic fashion. From each sample, or replicate, the estimate of interest is computed, and the variability of the estimate from the full data set is approximated by a simple function of the variability among the replicate-specific estimates. An appealing feature is that there is generally only one variance formula per method, regardless of the underlying quantity being estimated. The entire process can be efficiently implemented after appending a series of replicate weights to the analysis data set. As will be shown, the SURVEY family of SAS/STAT^® procedures can be exploited to facilitate both the task of appending the replicate weights and approximating variances.

This paper presents an application of the SURVEYSELECT procedure. The objective is to draw a systematic random sample from financial data for review. Topics covered in this paper include a brief review of systematic sampling, variable definitions, serpentine sorting, and an interpretation of the output.

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Understanding organizational trends in spending can help overseeing government agencies make appropriate modifications in spending to best serve the organization and the citizenry. However, given millions of line items for organizations annually, including free-form text, it is unrealistic for these overseeing agencies to succeed by using only a manual approach to this textual data. Using a publicly available data set, this paper explores how business users can apply text analytics using SAS^® Contextual Analysis to assess trends in spending for particular agencies, apply subject matter expertise to refine these trends into a taxonomy, and ultimately, categorize the spending for organizations in a flexible, user-friendly manner. SAS^® Visual Analytics enables dynamic exploration, including modeling results from SAS^® Visual Statistics, in order to assess areas of potentially extraneous spending, providing actionable information to the decision makers.

A leading killer in the United States is smoking. Moreover, over 8.6 million Americans live with a serious illness caused by smoking or second-hand smoking. Despite this, over 46.6 million U.S. adults smoke tobacco, cigars, and pipes. The key analytic question in this paper is, How would e-cigarettes affect this public health situation? Can monitoring public opinions of e-cigarettes using SAS^® Text Analytics and SAS^® Visual Analytics help provide insight into the potential dangers of these new products? Are e-cigarettes an example of Big Tobacco up to its old tricks or, in fact, a cessation product? The research in this paper was conducted on thousands of tweets from April to August 2014. It includes API sources beyond Twitter--for example, indicators from the Health Indicators Warehouse (HIW) of the Centers for Disease Control and Prevention (CDC)--that were used to enrich Twitter data in order to implement a surveillance system developed by SAS^® for the CDC. The analysis is especially important to The Office of Smoking and Health (OSH) at the CDC, which is responsible for tobacco control initiatives that help states to promote cessation and prevent initiation in young people. To help the CDC succeed with these initiatives, the surveillance system also: 1) automates the acquisition of data, especially tweets; and 2) applies text analytics to categorize these tweets using a taxonomy that provides the CDC with insights into a variety of relevant subjects. Twitter text data can help the CDC look at the public response to the use of e-cigarettes, and examine general discussions regarding smoking and public health, and potential controversies (involving tobacco exposure to children, increasing government regulations, and so on). SAS^® Content Categorization helps health care analysts review large volumes of unstructured data by categorizing tweets in order to monitor and follow what people are saying and why they are saying it. Ultimatel y, it is a solution intended to help the CDC monitor the public's perception of the dangers of smoking and e-cigarettes, in addition, it can identify areas where OSH can focus its attention in order to fulfill its mission and track the success of CDC health initiatives.

Several U.S. Federal agencies conduct national surveys to monitor health status of residents. Many of these agencies release their survey data to the public. Investigators might be able to address their research objectives by conducting secondary statistical analyses with these available data sources. This paper describes the steps in using the SAS SURVEY procedures to analyze publicly released data from surveys that use probability sampling to make statistical inference to a carefully defined population of elements (the target population).

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Sampling for audits and forensics presents special challenges: Each survey/sample item requires examination by a team of professionals, so sample size must be contained. Surveys involve estimating--not hypothesis testing. So power is not a helpful concept. Stratification and modeling is often required to keep sampling distributions from being skewed. A precision of alpha is not required to create a confidence interval of 1-alpha, but how small a sample is supportable? Many times replicated sampling is required to prove the applicability of the design. Given the robust, programming-oriented approach of SAS^®, the random selection, stratification, and optimizing techniques built into SAS can be used to bring transparency and reliability to the sample design process. While a sample that is used in a published audit or as a measure of financial damages must endure a special scrutiny, it can be a rewarding process to design a sample whose performance you truly understand and which will stand up under a challenge.

The vast and increasing demands of fraud detection and description have promoted the broad application of statistics and machine learning in fields as diverse as banking, credit card application and usage, insurance claims, trader surveillance, health care claims, and government funding and allowance management. SAS^® Visual Scenario Designer enables you to derive interactive business rules, along with descriptive and predictive models, to detect and describe fraud. This paper focuses on building interactive decision trees to classify fraud. Attention to optimizing the feature space (candidate predictors) prior to modeling is also covered. Because big data plays an increasingly vital role in fraud detection and description, SAS Visual Scenario Designer leverages the in-memory, parallel, and distributed computing abilities of SAS^® LASR™ Analytic Server as a back end to support real-time performance on massive amounts of data.

SAS^® PROC FASTCLUS generates five clusters for the group of repeat clients of Ontario's Remedial Measures program. Heat map tables are shown for selected variables such as demographics, scales, factor, and drug use to visualize the difference between clusters.

Throughout the latter part of the twentieth century, the United States of America has experienced an incredible boom in the rate of incarceration of its citizens. This increase arguably began in the 1970s when the Nixon administration oversaw the beginning of the war on drugs in America. The U.S. now has one of the highest rates of incarceration among industrialized nations. However, the citizens who have been incarcerated on drug charges have disproportionately been African American or other racial minorities, even though many studies have concluded that drug use is fairly equal among racial groups. In order to remedy this situation, it is essential to first understand why so many more people have been arrested and incarcerated. In this research, I explore a potential explanation for the epidemic of mass incarceration. I intend to answer the question does gubernatorial rhetoric have an effect on the rate of incarceration in a state? More specifically, I am interested in examining the language that the governor of a state uses at the annual State of the State address in order to see if there is any correlation between rhetoric and the subsequent rate of incarceration in that state. In order to understand any possible correlation, I use SAS^® Text Miner and SAS^® Contextual Analysis to examine the attitude towards crime in each speech. The political phenomenon that I am trying to understand is how state government employees are affected by the tone that the chief executive of a state uses towards crime, and whether the actions of these state employees subsequently lead to higher rates of incarceration. The governor is the top government official in charge of employees of a state, so when this official addresses the state, the employees may take the governor's message as an order for how they do their jobs. While many political factors can affect legislation and its enforcement, a governor has the ability to set the tone of a state when it comes to policy issues suc h as crime.

Many SAS^® procedures can be used to analyze longitudinal data. This study employed a multisite randomized controlled trial design to demonstrate the effectiveness of two SAS procedures, GLIMMIX and GENMOD, to analyze longitudinal data from five Department of Veterans Affairs Medical Centers (VAMCs). Older male veterans (n = 1222) seen in VAMC primary care clinics were randomly assigned to two behavioral health models, integrated (n = 605) and enhanced referral (n = 617). Data was collected at baseline, and at 3-, 6-, and 12- month follow-up. A mixed-effects repeated measures model was used to examine the dependent variable, problem drinking, which was defined as count and dichotomous from baseline to 12 month follow-up. Sociodemographics and depressive symptoms were included as covariates. First, bivariate analyses included general linear model and chi-square tests to examine covariates by group and group by problem drinking outcomes. All significant covariates were included in the GLIMMIX and GENMOD models. Then, multivariate analysis included mixed models with Generalized Estimation Equations (GEEs). The effect of group, time, and the interaction effect of group by time were examined after controlling for covariates. Multivariate results were inconsistent for GLIMMIX and GENMOD using Lognormal, Gaussian, Weibull, and Gamma distributions. SAS is a powerful statistical program in data analyses for longitudinal study.

Competing risks arise in studies in which individuals are subject to a number of potential failure events and the occurrence of one event might impede the occurrence of other events. For example, after a bone marrow transplant, a patient might experience a relapse or might die while in remission. You can use one of the standard methods of survival analysis, such as the log-rank test or Cox regression, to analyze competing-risks data, whereas other methods, such as the product-limit estimator, might yield biased results. An increasingly common practice of assessing the probability of a failure in competing-risks analysis is to estimate the cumulative incidence function, which is the probability subdistribution function of failure from a specific cause. This paper discusses two commonly used regression approaches for evaluating the relationship of the covariates to the cause-specific failure in competing-risks data. One approach models the cause-specific hazard, and the other models the cumulative incidence. The paper shows how to use the PHREG procedure in SAS/STAT^® software to fit these models.

Enrollment management is very important to all colleges. Having the correct tools to help you better understand your enrollment patterns of the past and the future is critical to any school. This session will describe how Valencia College went from manually updating static charts for enrollment management, to building dynamic, interactive visualizations to compare how students register across different calendar-date periods (current versus previous period)grouped by different start-of-registration dates--from start of registration, days into registration, and calendar date to previous year calendar date. This includes being able to see the trend by college campus, instructional method mode (onsite or online ) or by type of session (part of semester, full, and so on) all available in one visual and sliced and diced via check lists. The trend loads 4-6 million rows of data nightly to the SAS^® LASR™ Analytics Server in a snap with no performance issues on the back-end or presentation visual. We will give a brief history of how we used to load data into Excel and manually build charts. Then we will describe the current environment, which is an automated approach through SAS^® Visual Analytics. We will show pictures of our old, static reports, and then show the audience the power and functionality of our new, interactive reports through SAS Visual Analytics.

The experiences of the programmer role in a large SAS^® shop are shared. Shortages in SAS programming talent tend to result in one SAS programmer doing all of the production programming within a unit in a shop. In a real-world example, management realized the problem and brought in new programmers to help do the work. The new programmers actually improved the existing programmers' programs. It became easier for the experienced programmers to complete other programming assignments within the unit. And, the different programs in the shop had a standard structure. As a result, all of the programmers had a clearer picture of the work involved and knowledge hoarding was eliminated. Experienced programmers were now available when great SAS code needed to be written. Yet, they were not the only programmers who could do the work! With multiple programmers able to do the same tasks, vacations were possible and didn't threaten deadlines. It was even possible for these programmers to be assigned other tasks outside of the unit and broaden their own skills in statistical production work.

Working with multiple data sources in SAS^® was not a straight forward thing until PROC FEDSQL was introduced in the SAS^® 9.4 release. Federated Query Language, or FEDSQL, is a vendor-independent language that provides a common SQL syntax to communicate across multiple relational databases without having to worry about vendor-specific SQL syntax. PROC FEDSQL is a SAS implementation of the FEDSQL language. PROC FEDSQL enables us to write federated queries that can be used to perform joins on tables from different databases with a single query, without having to worry about loading the tables into SAS individually and combining them using DATA steps and PROC SQL statements. The objective of this paper is to demonstrate the working of PROC FEDSQL to fetch data from multiple data sources such as Microsoft SQL Server database, MySQL database, and a SAS data set, and run federated queries on all the data sources. Other powerful features of PROC FEDSQL such as transactions and FEDSQL pass-through facility are discussed briefly.