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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The paper introduces users to how they can use a set of SAS^® macros, %LIFETEST and %LIFETESTEXPORT, to generate survival analysis reports for data with or without competing risks. The macros provide a wrapper of PROC LIFETEST and an enhanced version of the SAS autocall macro %CIF to give users an easy-to-use interface to report both survival estimates and cumulative incidence estimates in a unified way. The macros also provide a number of parameters to enable users to flexibly adjust how the final reports should look without the need to manually input or format the final reports.

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Based on work by Thall et al. (2012), we implement a method for randomizing patients in a Phase II trial. We accumulate evidence that identifies which dose(s) of a cancer treatment provide the most desirable profile, per a matrix of efficacy and toxicity combinations rated by expert oncologists (0-100). Experts also define the region of Good utility scores and criteria of dose inclusion based on toxicity and efficacy performance. Each patient is rated for efficacy and toxicity at a specified time point. Simulation work is done mainly using PROC MCMC in which priors and likelihood function for joint outcomes of efficacy and toxicity are defined to generate posteriors. Resulting joint probabilities for doses that meet the inclusion criteria are used to calculate the mean utility and probability of having Good utility scores. Adaptive randomization probabilities are proportional to the probabilities of having Good utility scores. A final decision of the optimal dose will be made at the end of the Phase II trial.

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.

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.

You've worked for weeks or even months to produce an analysis suite for a project. Then, at the last moment, someone wants a subgroup analysis, and they inform you that they need it yesterday. This should be easy to do, right? So often, the programs that we write fall apart when we use them on subsets of the original data. This paper takes a look at some of the best practice techniques that can be built into a program at the beginning, so that users can subset on the fly without losing categories or creating errors in statistical tests. We review techniques for creating tables and corresponding titles with BY-group processing so that minimal code needs to be modified when more groups are created. And we provide a link to sample code and sample data that can be used to get started with this process.

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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In Bayesian statistics, Markov chain Monte Carlo (MCMC) algorithms are an essential tool for sampling from probability distributions. PROC MCMC is useful for these algorithms. However, it is often desirable to code an algorithm from scratch. This is especially present in academia where students are expected to be able to understand and code an MCMC. The ability of SAS^® to accomplish this is relatively unknown yet quite straightforward. We use SAS/IML^® to demonstrate methods for coding an MCMC algorithm with examples of a Gibbs sampler and Metropolis-Hastings random walk.

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.

Statistical analysis that uses data from clinical or epidemiological studies include continuous variables such as patient's age, blood pressure, and various biomarkers. Over the years, there has been an increase in studies that focus on assessing associations between biomarkers and disease of interest. Many of the biomarkers are measured as continuous variables. Investigators seek to identify the possible cutpoint to classify patients as high risk versus low risk based on the value of the biomarker. Several data-oriented techniques such as median and upper quartile, and outcome-oriented techniques based on score, Wald, and likelihood ratio tests are commonly used in the literature. Contal and O'Quigley (1999) presented a technique that used log rank test statistic in order to estimate the cutpoint. Their method was computationally intensive and hence was overlooked due to the unavailability of built-in options in standard statistical software. In 2003, we provided the %FINDCUT macro that used Contal and O'Quigley's approach to identify a cutpoint when the outcome of interest was measured as time to event. Over the past decade, demand for this macro has continued to grow, which has led us to consider updating the %FINDCUT macro to incorporate new tools and procedures from SAS^® such as array processing, Graph Template Language, and the REPORT procedure. New and updated features include: results presented in a much cleaner report format, user-specified cutpoints, macro parameter error checking, temporary data set cleanup, preserving current option settings, and increased processing speed. We present the utility and added options of the revised %FINDCUT macro using a real-life data set. In addition, we critically compare this method to some of the existing methods and discuss the use and misuse of categorizing a continuous covariate.

Data sharing through healthcare collaboratives and national registries creates opportunities for secondary data analysis projects. These initiatives provide data for quality comparisons as well as endless research opportunities to external researchers across the country. The possibilities are bountiful when you join data from diverse organizations and look for common themes related to illnesses and patient outcomes. With these great opportunities comes great pain for data analysts and health services researchers tasked with compiling these data sets according to specifications. Patient care data is complex, and, particularly at large healthcare systems, might be managed with multiple electronic health record (EHR) systems. Matching data from separate EHR systems while simultaneously ensuring the integrity of the details of that care visit is challenging. This paper demonstrates how data management personnel can use traditional SAS PROCs in new and inventive ways to compile, clean, and complete data sets for submission to healthcare collaboratives and other data sharing initiatives. Traditional data matching methods such as SPEDIS are uniquely combined with iterative SQL joins using the SAS^® functions INDEX, COMPRESS, CATX, and SUBSTR to yield the most out of complex patient and physician name matches. Recoding, correcting missing items, and formatting data can be efficiently achieved by using traditional functions such as MAX, PROC FORMAT, and FIND in new and inventive ways.

In many studies, a continuous response variable is repeatedly measured over time on one or more subjects. The subjects might be grouped into different categories, such as cases and controls. The study of resulting observation profiles as functions of time is called functional data analysis. This paper shows how you can use the SSM procedure in SAS/ETS^® software to model these functional data by using structural state space models (SSMs). A structural SSM decomposes a subject profile into latent components such as the group mean curve, the subject-specific deviation curve, and the covariate effects. The SSM procedure enables you to fit a rich class of structural SSMs, which permit latent components that have a wide variety of patterns. For example, the latent components can be different types of smoothing splines, including polynomial smoothing splines of any order and all L-splines up to order 2. The SSM procedure efficiently computes the restricted maximum likelihood (REML) estimates of the model parameters and the best linear unbiased predictors (BLUPs) of the latent components (and their derivatives). The paper presents several real-life examples that show how you can fit, diagnose, and select structural SSMs; test hypotheses about the latent components in the model; and interpolate and extrapolate these latent components.

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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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Over the years, very few published studies have discussed ways to improve the performance of two-stage predictive models. This study, based on 10 years (1999-2008) of data from 130 US hospitals and integrated delivery networks, is an attempt to demonstrate how we can leverage the Association node in SAS^® Enterprise Miner™ to improve the classification accuracy of the two-stage model. We prepared the data with imputation operations and data cleaning procedures. Variable selection methods and domain knowledge were used to choose 43 key variables for the analysis. The prominent association rules revealed interesting relationships between prescribed medications and patient readmission/no-readmission. The rules with lift values greater than 1.6 were used to create dummy variables for use in the subsequent predictive modeling. Next, we used two-stage sequential modeling, where the first stage predicted if the diabetic patient was readmitted and the second stage predicted whether the readmission happened within 30 days. The backward logistic regression model outperformed competing models for the first stage. After including dummy variables from an association analysis, many fit indices improved, such as the validation ASE to 0.228 from 0.238, cumulative lift to 1.56 from 1.40. Likewise, the performance of the second stage was improved after including dummy variables from an association analysis. Fit indices such as the misclassification rate improved to 0.240 from 0.243 and the final prediction error to 0.17 from 0.18.

The research areas of pharmaceuticals and oncology clinical trials greatly depend on time-to-event endpoints such as overall survival and progression-free survival. One of the best graphical displays of these analyses is the Kaplan-Meier curve, which can be simple to generate with the LIFETEST procedure but difficult to customize. Journal articles generally prefer that statistics such as median time-to-event, number of patients, and time-point event-free rate estimates be displayed within the graphic itself, and this was previously difficult to do without an external program such as Microsoft Excel. The macro %NEWSURV takes advantage of the Graph Template Language (GTL) that was added with the SG graphics engine to create this level of customizability without the need for back-end manipulation. Taking this one step further, the macro was improved to be able to generate a lattice of multiple unique Kaplan-Meier curves for side-by-side comparisons or for condensing figures for publications. This paper describes the functionality of the macro and describes how the key elements of the macro work.

A forest plot is a common visualization for meta-analysis. Some popular versions that use subgroups with indented text and bold fonts can seem outright daunting to create. With SAS^® 9.4, the Graph Template Language (GTL) has introduced the AXISTABLE statement, specifically designed for including text data columns into a graph. In this paper, we demonstrate the simplicity of creating various forest plots using AXISTABLE statements. Come and see how to create forest plots as clear as day!

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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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Differential item functioning (DIF), as an assessment tool, has been widely used in quantitative psychology, educational measurement, business management, insurance, and health care. The purpose of DIF analysis is to detect response differences of items in questionnaires, rating scales, or tests across different subgroups (for example, gender) and to ensure the fairness and validity of each item for those subgroups. The goal of this paper is to demonstrate several ways to conduct DIF analysis by using different SAS^® procedures (PROC FREQ, PROC LOGISITC, PROC GENMOD, PROC GLIMMIX, and PROC NLMIXED) and their applications. There are three general methods to examine DIF: generalized Mantel-Haenszel (MH), logistic regression, and item response theory (IRT). The SAS^® System provides flexible procedures for all these approaches. There are two types of DIF: uniform DIF, which remains consistent across ability levels, and non-uniform DIF, which varies across ability levels. Generalized MH is a nonparametric method and is often used to detect uniform DIF while the other two are parametric methods and examine both uniform and non-uniform DIF. In this study, I first describe the underlying theories and mathematical formulations for each method. Then I show the SAS statements, input data format, and SAS output for each method, followed by a detailed demonstration of the differences among the three methods. Specifically, PROC FREQ is used to calculate generalized MH only for dichotomous items. PROC LOGISITIC and PROC GENMOD are used to detect DIF by using logistic regression. PROC NLMIXED and PROC GLIMMIX are used to examine DIF by applying an exploratory item response theory model. Finally, I use SAS/IML^® to call two R packages (that is, difR and lordif) to conduct DIF analysis and then compare the results between SAS procedures and R packages. An example data set, the Verbal Aggression assessment, which includes 316 subjects and 24 items, is used in this stud y. Following the general DIF analysis, the male group is used as the reference group, and the female group is used as the focal group. All the analyses are conducted by SAS^® 9.3 and R 2.15.3. The paper closes with the conclusion that the SAS System provides different flexible and efficient ways to conduct DIF analysis. However, it is essential for SAS users to understand the underlying theories and assumptions of different DIF methods and apply them appropriately in their DIF analyses.

SAS^® SQL is so powerful that you hardly miss using Oracle PL/SQL. One SAS SQL forte can be found in using the SQL reflexive join. Another area of SAS SQL strength is the SQL subquery concept. The focus of this paper is to show alternative approaches to data reporting and to show how to surface data quality problems using reflexive join and subquery SQL concepts. The target audience for this paper is the intermediate SAS programmer or the experienced ANSI SQL programmer new to SAS programming.

Researchers, patients, clinicians, and other health-care industry participants are forging new models for data-sharing in hopes that the quantity, diversity, and analytic potential of health-related data for research and practice will yield new opportunities for innovation in basic and translational science. Whether we are talking about medical records (for example, EHR, lab, notes), administrative data (claims and billing), social (on-line activity), behavioral (fitness trackers, purchasing patterns), contextual (geographic, environmental), or demographic data (genomics, proteomics), it is clear that as health-care data proliferates, threats to security grow. Beginning with a review of the major health-care data breeches in our recent history, we highlight some of the lessons that can be gleaned from these incidents. In this paper, we talk about the practical implications of data sharing and how to ensure that only the right people have the right access to the right level of data. To that end, we explore not only the definitions of concepts like data privacy, but we discuss, in detail, methods that can be used to protect data--whether inside our organization or beyond its walls. In this discussion, we cover the fundamental differences between encrypted data, 'de-identified', 'anonymous', and 'coded' data, and methods to implement each. We summarize the landscape of maturity models that can be used to benchmark your organization's data privacy and protection of sensitive data.

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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.

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.

Surveys are designed to elicit information about population characteristics. A survey design typically combines stratification and multistage sampling of intact clusters, sub-clusters, and individual units with specified probabilities of selection. A survey sample can produce valid and reliable estimates of population parameters at a fraction of the cost of carrying out a census of the entire population, with clear logistical efficiencies. For analyses of survey data, SAS^®software provides a suite of procedures from SURVEYMEANS and SURVEYFREQ for generating descriptive statistics and conducting inference on means and proportions to regression-based analysis through SURVEYREG and SURVEYLOGISTIC. For longitudinal surveys and follow-up studies, SURVEYPHREG is designed to incorporate aspects of the survey design for analysis of time-to-event outcomes based on the Cox proportional hazards model, allowing for time-varying explanatory variables.We review the salient features of the SURVEYPHREG procedure with application to survey data from the National Health and Nutrition Examination Survey (NHANES III) Linked Mortality File.

This session describes our journey from data acquisition to text analytics on clinical, textual data.

Researchers often use longitudinal data analysis to study the development of behaviors or traits. For example, they might study how an elderly person's cognitive functioning changes over time or how a therapeutic intervention affects a certain behavior over a period of time. This paper introduces the structural equation modeling (SEM) approach to analyzing longitudinal data. It describes various types of latent curve models and demonstrates how you can use the CALIS procedure in SAS/STAT^® software to fit these models. Specifically, the paper covers basic latent curve models, such as unconditional and conditional models, as well as more complex models that involve multivariate responses and latent factors. All illustrations use real data that were collected in a study that looked at maternal stress and the relationship between mothers and their preterm infants. This paper emphasizes the practical aspects of longitudinal data analysis. In addition to illustrating the program code, it shows how you can interpret the estimation results and revise the model appropriately. The final section of the paper discusses the advantages and disadvantages of the SEM approach to longitudinal data analysis.

Abalone is a common name given to sea snails or mollusks. These creatures are highly iridescent, with shells of strong changeable colors. This characteristic makes the shells attractive to humans as decorative objects and jewelry. The abalone structure is being researched to build body armor. The value of a shell varies by its age and the colors it displays. Determining the number of rings on an abalone is a tedious and cumbersome task and is usually done by cutting the shell through the cone, staining it, and counting the number of rings on it through a microscope. In this poster, I aim to predict the number of any rings on any abalone by using the physical characteristics. This data was obtained from UCI Machine Learning Repository, which consists of 4,177 observations with 8 attributes. I considered the number of rings to be my target variable. The abalone's age can be reasonably approximated as being 1.5 times the number of rings on its shell. Using SAS^® Enterprise Miner™, I have built regression models and neural network models to determine the physical measurements responsible for determining the number of rings on the abalone. While I have obtained a coefficient of determination of 54.01%, my aim is to improve and expand the analysis using the power of SAS Enterprise Miner. The current initial results indicate that the height, the shucked weight, and the viscera weight of the shell are the three most influential variables in predicting the number of rings on an abalone.

Many epidemiological studies use medical claims to identify and describe a population. But finding out who was diagnosed, and who received treatment, isn't always simple. Each claim can have dozens of medical codes, with different types of codes for procedures, drugs, and diagnoses. Even a basic definition of treatment could require a search for any one of 100 different codes. A SAS^® macro may come to mind, but generalizing the macro to work with different codes and types allows it to be reused in a variety of different scenarios. We look at a number of examples, starting with a single code type and variable. Then we consider multiple code variables, multiple code types, and multiple flag variables. We show how these macros can be combined and customized for different data with minimal rework. Macro flexibility and reusability are also discussed, along with ways to keep our list of medical codes separate from our program. Finally, we discuss time-dependent medical codes, codes requiring database lookup, and macro performance.

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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.

Data visualization is synonymous with big data, for which billions of records and millions of variables are analyzed simultaneously. But that does not mean data scientists analyzing clinical trial data that include only thousands of records and hundreds of variables cannot take advantage of data visualization methodologies. This paper presents a comprehensive process for loading standard clinical trial data into SAS^® Visual Analytics, an interactive analytic solution. The process implements template reporting for a wide variety of point-and-click visualizations. Data operations required to support this reporting are explained and examples of the actual visualizations are presented so that users can implement this reporting using their own data.

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.