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Statistics Papers and Presentations

2020 Papers

• Causal Effect Estimands: Interpretation, Identification, and Computation
  Clay Thompson, Michael Lamm, and Yiu-Fai Yung; SAS Institute, Inc. 2020

  This paper provides an overview of different types of causal estimands, a comparison of how the different estimands are interpreted, and guidance on how identifying an appropriate estimand can help you determine an appropriate causal analysis workflow. The CAUSALGRAPH, CAUSALMED, CAUSALTRT, and PSMATCH procedures in SAS/STAT software are used to demonstrate the workflow. The paper also includes a review of the assumptions that are required for identifying and estimating causal effects.

• Practical Geospatial Analysis of Open and Public-Use Data
  Pradeep Mohan; SAS Institute, Inc. 2020

  This paper presents a variety of real-world examples of public-use geospatial data to show you how to transform the data into a prespecified geographic reference, import the data into SAS Viya via a Python interface, and select suitable subsets that can be convenient for data analysis.

• Recent Developments in Survival Analysis with SAS Software
  G. Gordon Brown; SAS Institute, Inc. 2020

  This paper brings you up to date on new approaches and procedures in SAS software and provides an overview on when to use these procedures to overcome the challenges inherent in conducting survival analysis in today’s world.

• A SAS Macro for Calibration of Survey Weights
  Tony An; SAS Institute, Inc. 2020

  This paper discusses a macro for calibration that was developed using SAS/STAT 15.1 and SAS/IML software. The macro enables you to input the design information, the controls for the auxiliary variables, and your preferred calibration method, including either a linear or exponential method. Because an unbounded calibration method can result in extreme calibration weights, this macro also supports bounded versions of both linear and exponential calibration methods. The macro creates calibration replication weights according to the sample design and the specified calibration method. Examples are given to illustrate how to use the macro.

• A Survey of Methods in Variable Selection and Penalized Regression
  Yingwei Wang; SAS Institute, Inc. 2020

  This paper reviews variable selection methods in linear regression, grouped into two categories: sequential methods, such as forward selection, backward elimination, and stepwise regression; and penalized methods, also called shrinkage or regularization methods, including the LASSO, elastic net, and so on. In addition to covering mathematical properties of the methods, the paper presents practical examples using SAS/STAT software and SAS Viya.

2019 Papers

• Analyzing Restricted Mean Survival Time Using SAS/STAT
  Guo, Changbin; Ying, So; SAS Institute, Inc. 2019

  This paper introduces RMST methods in SAS/STAT software: you can now use the RMSTREG procedure to fit linear and log-linear models, and you can use the RMST option in PROC LIFETEST to estimate the restricted mean survival time and make comparisons between groups.

• Building a Propensity Score Model with SAS/STAT Software: Planning and Practice
  Lamm, Michael; Thompson, Clay; Yung, Yiu-Fai; SAS Institute, Inc. 2019

  This paper discusses how you can use the PSMATCH procedure in conjunction with other procedures in SAS/STAT software to tackle some of these practical challenges. In particular, the paper demonstrates how you can use causal graphs to investigate questions related to ignorability and how you can incorporate propensity scores that are computed using approaches other than logistic regression.

• Causal Graph Analysis with the CAUSALGRAPH Procedure
  Thompson, Clay; SAS Institute, Inc. 2019

  This paper introduces the CAUSALGRAPH procedure, new in SAS/STAT® 15.1, for analyzing graphical causal models.

• Introducing the BGLIMM Procedure for Bayesian Generalized Linear Mixed Models
  Shi, Amy; Chen, Fang; SAS Institute, Inc. 2019

  This paper describes how to use the BGLIMM procedure for estimation, inference, and prediction.

• Predicting Inside the Dead Zone of Complete Separation in Logistic Regression
  Derr, Robert; SAS Institute, Inc. 2019

  Logistic regression is a generalized linear model that you can use to predict which of two response levels is more likely for a given observation. A logistic regression program can fail for a number of reasons, but even when it does not fail, other problems can appear. This paper focuses on one of those problems: complete separation and the existence of a dead zone in your data

• SCORE! Techniques for Scoring Predictive Regression Models Using SAS/STAT Software
  Gibbs, Phil; Tobias, Randy; SAS Institute, Inc. 2019

  The paper discusses practical applications for scoring with the SCORE statement within a modeling procedure, with the PLM procedure using a stored model fit, and with the CODE statement in the DATA step.

2018 Papers

• Causal Mediation Analysis with the CAUSALMED Procedure
  Lamm, Michael; Yung, Yiu-Fai; Zhang, Wei; SAS Institute, Inc. 2018

  This paper introduces the CAUSALMED procedure, new in SAS/STAT 14.3, for estimating various causal mediation effects from observational data in a counterfactual framework. The paper also defines these causal mediation and related effects in terms of counterfactual outcomes and describes the assumptions that are required for unbiased estimation. Examples illustrate the ideas behind causal mediation analysis and the applications of the CAUSALMED procedure.

• Cause-Specific Analysis of Competing Risks Using the PHREG Procedure
  Guo, Changbin; So, Ying; SAS Institute, Inc. 2018

  SAS/STAT 14.3 includes updates to the PHREG procedure to perform the cause-specific analysis of competing risks. This paper describes how cause-specific hazard regression works and compares it to the Fine and Gray method. Examples illustrate how to interpret the models appropriately and how to obtain predicted cumulative incidence functions.

• Fitting Compartment Models Using PROC NLMIXED
  Kurada, Raghavendra: Chen, Fang; SAS Institute, Inc. 2018

  The CMPTMODEL statement is a new enhancement to the NLMIXED procedure in SAS/STAT 14.3. This statement enables you to fit a large class of pharmacokinetics (PK) models, including one-, two-, and three-compartment models, with intravenous (bolus and infusion) and extravascular (oral) types of drug administration. The CMPTMODEL statement also supports multiple dosages and PK models that have various parameterizations. This paper introduces the new statement and illustrates its usage through examples.

• Insights into Using the GLIMMIX Procedure to Model Categorical Outcomes with Random Effects
  Kiernan, Kathleen; SAS Institute, Inc. 2018

  Modeling categorical outcomes with random effects is a major use of the GLIMMIX procedure. Building, evaluating, and using the resulting model for inference, prediction, or both requires many considerations. This paper, written for experienced users of SAS statistical procedures, illustrates the nuances of the process with two examples: modeling a binary response using random effects and correlated errors and modeling a multinomial response with random effects.

• Power Analysis for Generalized Linear Models Using the New CUSTOM Statement in PROC POWER
  Castelloe, John; SAS Institute, Inc. 2018

  Modeling categorical outcomes with random effects is a major use of the GLIMMIX procedure. Building, evaluating, and using the resulting model for inference, prediction, or both requires many considerations. This paper, written for experienced users of SAS statistical procedures, illustrates the nuances of the process with two examples: modeling a binary response using random effects and correlated errors and modeling a multinomial response with random effects.

• Regression Model Building for Large, Complex Data with SAS Viya Procedures
  Rodriguez, Robert N.; Cai, Weijie; SAS Institute, Inc. 2018

  This paper describes SAS Viya procedures for building linear and logistic regression models, generalized linear models, quantile regression models, generalized additive models, and proportional hazards regression models. It also explains how these procedures capitalize on the in-memory environment of SAS Viya, and it compares their syntax, features, and output with those of high-performance regression modeling procedures in SAS/STAT software.

• SAS/STAT 14.3: Roundup: Modern Methods for the Modern Statistician
  Stokes, Maura; SAS Institute, Inc. 2018

  The latest release of SAS/STAT software has something for everyone. The new CAUSALMED procedure performs causal mediation analysis for observational data, enabling you to obtain unbiased estimates of the direct causal effect. You can now fit compartment models for pharmacokinetic analysis with the NLMIXED and MCMC procedures. In addition, variance estimation by the bootstrap method is available in the survey data analysis procedures, and the PHREG procedure provides cause-specific proportional hazards analysis for competing-risk data. Several other procedures have been enhanced as well.

2017 Papers

• Advanced Hierarchical Modeling with the MCMC Procedure
  Chen, Fang; Stokes, Maura; SAS Institute, Inc. 2017

  This paper shows how you can use PROC MCMC to fit hierarchical models that have varying degrees of complexity, from frequently encountered conditional independent models to more involved cases of modeling intricate interdependence.

• Estimating Causal Effects from Observational Data with the CAUSALTRT Procedure
  Lamm, Michael; Yung, Yiu-Fai; SAS Institute, Inc. 2017

  This paper reviews the statistical methods that are implemented in the CAUSALTRT procedure and includes examples of how you can use this procedure to estimate causal effects from observational data.

• Evaluating Predictive Accuracy of Survival Models with PROC PHREG
  Guo, Changbin; So, Ying; Woosung, Jang; SAS Institute, Inc. 2017

  This paper reviews the existing features in PROC LOGISTIC for C-statistic and ROC curves, presents the new features in PROC PHREG, and illustrates their applications in examples. Key differences between PROC PHREG and PROC LOGISTIC are also examined.

• Five Things You Should Know about Quantile Regression
  Rodriguez, Robert N.; Yao, Yonggang; SAS Institute, Inc. 2017

  This paper explains the concepts and benefits of quantile regression, and it introduces you to the appropriate procedures in SAS/STAT software.

• Propensity Score Methods for Causal Inference with the PSMATCH Procedure
  Yuan, Yang; Yung, Yiu-Fai; Stokes, Maura; SAS Institute, Inc. 2017

  This paper reviews propensity score methods for causal inference and introduces the PSMATCH procedure, which is new in SAS/STAT 14.2.

• Step Up Your Statistical Practice with Today’s SAS/STAT Software
  Rodriguez, Robert N.; Gibbs, Phil; Tobias, Randy; SAS Institute, Inc. 2017

  This paper will increase your awareness of modern tools in SAS/STAT by providing high-level comparisons with well-established tools and explaining the benefits of enhancements and new procedures.

2016 Papers

• Fitting Multilevel Hierarchical Mixed Models Using PROC NLMIXED
  Kurada, Raghavendra Rao; SAS Institute, Inc. 2016

  The NLMIXED procedure’s ability to fit linear and nonlinear models with standard or general distributions enables you to fit a wide range of such models. SAS/STAT® 13.2 enhanced PROC NLMIXED to support multiple RANDOM statements, enabling you to fit nested multilevel mixed models. This paper uses an example to illustrate the new functionality.

• Fitting Your Favorite Mixed Models with PROC MCMC
  Chen, Fang; Brown, Gordon; Stokes, Maura; SAS Institute, Inc. 2016

  This paper describes how to use the MCMC procedure to fit Bayesian mixed models and compares the Bayesian approach to how the classical models would be fit with the familiar mixed modeling procedures.

• Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software
  Rodriguez, Robert N.; SAS Institute, Inc. 2016

  This paper provides a high-level tour of five modern approaches to model building that are available in recent releases of SAS/STAT: building sparse regression models with the GLMSELECT procedure, building generalized linear models with the HPGENSELECT procedure, building quantile regression models with the QUANTSELECT procedure, fitting generalized additive models with the GAMPL procedure, and building classification and regression trees with the HPSPLIT procedure

• Survey Data Imputation with PROC SURVEYIMPUTE
  Mukhopadhyay, Pushpal; SAS Institute, Inc. 2016

  This paper discusses different approaches for handling nonresponse in surveys, introduces PROC SURVEYIMPUTE, and demonstrates its use with real-world applications. It also discusses connections with other ways of handling missing values in SAS/STAT.

• Tips and Strategies for Mixed Modeling with SAS/STAT Procedures
  Tao, Jill; Kiernan, Kathleen; Gibbs, Phil; SAS Institute, Inc. 2016

  This paper provides recommendations for circumventing memory problems and reducing execution times for your mixed-modeling analyses. It also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, it focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software.

2015 Papers

• Advanced Techniques for Fitting Mixed Models Using SAS/STAT Software
  Tao, Jill; Kiernan, Kathleen; SAS Institute, Inc. 2015

  This paper provides guidance on how to overcome obstacles that commonly occur when you fit mixed models using the MIXED and GLIMMIX procedures.

• Analyzing Spatial Point Patterns Using the New SPP Procedure
  Mohan, Pradeep; Tobias, Randy; SAS Institute, Inc. 2015

  This paper describes the SPP procedure, new in SAS/STAT 13.2, for exploring and modeling spatial point pattern data.

• Encore: Introduction to Bayesian Analysis Using SAS/STAT
  Stokes, Maura; Chen, Fang; Güneş, Funda; SAS Institute, Inc. 2015

  This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then describes the built-in Bayesian capabilities provided in SAS/STAT, which became available for all platforms with SAS/STAT 9.3, with examples from the GENMOD and PHREG procedures.

• Equivalence and Noninferiority Testing Using SAS/STAT Software
  Castelloe, John; Watts, Donna; SAS Institute, Inc. 2015

  This paper reviews familiar and new methods for planning and analyzing equivalence and noninferiority studies in the POWER, TTEST, and FREQ procedures in SAS/STAT software.

• Introducing the HPGENSELECT Procedure: Model Selection for Generalized Linear Models and More
  Johnston, Gordon; Rodriguez, Robert N.; SAS Institute, Inc. 2015

  This paper shows you how to use the HPGENSELECT procedure both for model selection and for fitting a single model. The paper also explains the differences between the HPGENSELECT procedure and the GENMOD procedure.

• Penalized Regression Methods for Linear Models in SAS/STAT
  Güneş, Funda; SAS Institute, Inc. 2015

  This paper first provides a brief review of the LASSO, adaptive LASSO, and elastic net penalized model selection methods. Then it explains how to perform model selection by applying these techniques with the GLMSELECT procedure, which includes extensive customization options and powerful graphs for steering statistical model selection.

• SAS/STAT 14.1: Methods for Massive, Missing, or Multifaceted Data
  Stokes, Maura; SAS Institute, Inc. 2015

  This paper describes the additions and provides examples of using the new capabilities for survey data imputation, logistic regression using adjacent-category logits, generalized additive models, and classification trees in SAS/STAT 14.1.

• The SEM Approach to Longitudinal Data Analysis Using the CALIS Procedure
  An, Xinming; Yung, Yiu-Fai; SAS Institute, Inc.; Yang, Qing; Duke University 2015

• Using the PHREG Procedure to Analyze Competing-Risks Data
  So, Ying; Lin, Guixian, Lin; Johnston, Gordon; SAS Institute, Inc. 2015

  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

2014 Papers

• Marginal Model Plots
  Kuhfeld, Warren; SAS Institute, Inc. 2014

  Marginal model plots enable you to evaluate model fit by comparing predicted values given all of the variables in the full model and a loess fit function for each independent variable. When the two functions are similar in each of the graphs, there is evidence that the model fits well. When the two functions differ in at least one of the graphs, there is evidence that the model does not fit well.

• Creating Path Diagrams That Impress: A New Graphical Capability of the CALIS Procedure
  Yung, Yiu-Fai; SAS Institute, Inc. 2014

  This paper illustrates how you can create and use path diagrams to present your model and statistical results. It also demonstrates the use of the ODS Graphics Editor to edit path diagrams that are produced from the CALIS procedure.

• Getting Started with the MCMC Procedure
  Güneş, Funda; Chen, Fang; SAS Institute, Inc. 2014

  This paper presents a series of examples that show how to use PROC MCMC to fit Bayesian models.

• Introducing the BCHOICE Procedure for Bayesian Discrete Choice Models
  McDowell, Allen; Shi, Amy; SAS Institute, Inc. 2014

  This paper discusses the new, experimental BCHOICE procedure in SAS/STAT 13.1 which enables you to perform Bayesian analysis for discrete choice models. PROC BCHOICE fits multinomial logit, nested logit, mixed multinomial logit, multinomial probit, and mixed multinomial probit models. Brief summaries of the properties of the various models are provided along with a series of examples that highlight the capabilities of PROC BCHOICE.

• Introducing the HPGENSELECT Procedure: Model Selection for Generalized Linear Models and More
  Johnston, Gordon; Rodriguez, Robert N.; SAS Institute, Inc. 2014

  This paper shows you how to use the HPGENSELECT procedure both for model selection and for fitting a single model. The paper also explains the differences between the HPGENSELECT procedure and the GENMOD procedure.

• Using the PHREG Procedure to Analyze Competing-Risks Data
  So, Ying; Lin, Guixian; Johnston, Gordon; SAS Institute, Inc. 2014

  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 to fit these models.

• Analyzing Interval-Censored Data with the ICLIFETEST Procedure
  Guo, Changbin; So, Ying; Johnston, Gordon ; SAS Institute, Inc. 2014

  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.

• Analyzing Multilevel Models with the GLIMMIX Procedure
  Zhu, Min; SAS Institute, Inc. 2014

  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.

• Case-Level Residual Analysis in the CALIS Procedure
  Truxillo, Catherine; SAS Institute, Inc. 2014

  This paper demonstrates the new case-level residuals in the CALIS procedure and how they differ from classic residuals in structural equation modeling (SEM).

• Examples of Logistic Modeling with the SURVEYLOGISTIC Procedure
  Agnelli, Rob; SAS Institute, Inc. 2014

  Through examples, this paper provides guidance in using PROC SURVEYLOGISTIC to apply logistic regression modeling techniques to data that are collected from a complex survey design.

• An Introduction to Bayesian Analysis with SAS/STAT Software
  Stokes, Maura; Chen, Fang; Güneş, Funda; SAS Institute, Inc. 2014

  This paper introduces the principles of Bayesian inference and reviews the steps in a Bayesian analysis. It then describes the built-in Bayesian capabilities provided in SAS/STAT, which became available for all platforms with SAS/STAT 9.3, with examples from the GENMOD and PHREG procedures.

• Is Nonlinear Regression Throwing You a Curve? New Diagnostic and Inference Tools in the NLIN Procedure
  Gebremariam, Biruk; SAS Institute, Inc. 2014

  This paper presents the the diagnostic and inferential features that were added to PROC NLIN in SAS/STAT® 9.3, 12.1, and 13.1and explains how to use them. This paper highlights how measures of nonlinearity help you diagnose models and decide on potential reparameterization.

• Item Response Theory: What It Is and How You Can Use the IRT Procedure to Apply It
  An, Xinming; Yung, Yiu-Fai; SAS Institute, Inc. 2014

  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.

• Making Comparisons Fair: How LS-Means Unify the Analysis of Linear Models
  Cai, Weijie; SAS Institute, Inc. 2014

  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.

• Power and Sample Size for MANOVA and Repeated Measures with the GLMPOWER Procedure
  Castelloe, John; SAS Institute, Inc. 2014

  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.

• SAS/STAT 13.1 Round-Up
  Rodriguez, Robert N.; Stokes, Maura; SAS Institute, Inc. 2014

  This paper provides overviews and introductory examples for each of the new focus areas in SAS/STAT 13.1. The paper also provides a sneak preview of the follow-up release, SAS/STAT 13.2, which brings additional strategies for missing data analysis and other important updates to statistical customers.

• A SAS Macro to Diagnose Influential Subjects in Longitudinal Studies
  Schneider, Grant W., The Ohio State University; Tobias, Randall D.; SAS Institute, Inc. 2014

  This paper presents the %SCDMixed SAS macro, which implements a generalization of Cook's Distance 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. Download the zip file.

• Sensitivity Analysis in Multiple Imputation for Missing Data
  Yuan, Yang; SAS Institute, Inc. 2014

  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.

• Weighted Methods for Analyzing Missing Data with the GEE Procedure
  Lin, Guixian; Rodriguez, Robert N.; SAS Institute, Inc. 2014

  This paper reviews the concepts and statistical methods. Examples illustrate how you can apply the GEE procedure to incomplete longitudinal data. Download the zip file.

2013 Papers

• Computing Direct and Indirect Standardized Rates and Risks with the STDRATE Procedure
  Yuan, Yang; SAS Institute, Inc. 2013

  This paper reviews the concepts of standardized rate and risk and introduces the STDRATE procedure, which is new in SAS/STAT 12.1. PROC STDRATE computes directly standardized rates and risks by using Mantel-Haenszel estimates, and it computes indirectly standardized rates and risks by using standardized morbidity/mortality ratios (SMR). PROC STDRATE also provides stratum-specific summary statistics, such as rate and risk estimates and confidence limits.

• Creating and Customizing the Kaplan-Meier Survival Plot in PROC LIFETEST
  Kuhfeld, Warren F.; So, Ying; SAS Institute, Inc. 2013

  This paper provides examples of survival plot modifications using procedure options, graph template modifications using macros, and style template modifications.

• Creating and Customizing the Kaplan-Meier Survival Plot in PROC LIFETEST - LARGE GRAPHICS
  Kuhfeld, Warren F.; So, Ying; SAS Institute, Inc. 2013

  This paper provides examples of survival plot modifications using procedure options, graph template modifications using macros, and style template modifications.

• Current Directions in SAS/STAT Software Development
  Stokes, Maura; SAS Institute, Inc. 2013

  This paper describes recent areas of development focus, such as Bayesian analysis, missing data methods, postfitting inference, quantile modeling, finite mixture models, specialized survival analysis, structural equation modeling, and spatial statistics. It also introduces you to the concepts and illustrates them with practical applications.

• Having an EFFECT: More General Linear Modeling and Analysis with the New EFFECT Statement in SAS/STAT Software
  Gibbs, Phil; Tobias, Randy; Kiernan, Kathleen; Tao, Jill; SAS Institute, Inc. 2013

  This paper first shows how the EFFECT statement fits into the general architecture of SAS/STAT linear modeling tools and then explains and demonstrates specific effect-types. You will see how this powerful new feature easily enhances the statistical analyses that you can perform.

• High-Performance Statistical Modeling
  Cohen, Robert A.; Rodriguez, Robert N.; SAS Institute, Inc. 2013

  This paper describes the various execution modes and data access methods for high-performance analytics procedures. It also discusses the design principles for high-performance statistical modeling procedures and offers guidance about how and when these procedures provide performance benefits.

• Introducing the New ADAPTIVEREG Procedure for Adaptive Regression
  Kuhfeld, Warren F.; Cai, Weijie; SAS Institute, Inc. 2013

  This paper shows you how to use PROC ADAPTIVEREG (a new SAS/STAT procedure for multivariate adaptive regression spline models) by presenting a series of examples that show the relationship between adaptive regression models and other statistical modeling techniques.

• Missing No More: Using the MCMC Procedure to Model Missing Data
  Chen, Fang; SAS Institute, Inc. 2013

  This paper reviews the Bayesian approach and describes how the MCMC procedure implements it.

• Ordinal Response Modeling with the LOGISTIC Procedure
  Derr, Bob; SAS Institute, Inc. 2013

  This paper describes how you can use the LOGISTIC procedure to model ordinal responses. Before SAS/STAT 12.1, you could use cumulative logit response functions with proportional odds. In SAS/STAT 12.1, you can fit partial proportional odds models to ordinal responses. This paper also discusses methods of determining which covariates have proportional odds.

• Scalability of the SAS/STAT HPGENSELECT High-Performance Analytical Procedure: A Comparison with RevoScaleR
  Thompson, Wayne; Ames, Jennifer; Ho, Dright; SAS Institute, Inc. 2013

  This paper compares the performance of the HPGENSELECT procedure with results cited for the RevoScaleR package by using data that are similar to the insurer's data. The paper also demonstrates the scalability of the HPGENSELECT procedure by using two sizes of data sets and three different computing environments.

• Using the QUANTLIFE Procedure for Survival Analysis
  Lin, Guixian; Rodriguez, Bob N.; SAS Institute, Inc. 2013

  This paper compares the quantile regression model with the Cox and accelerated failure time models, which are commonly used in survival analysis.

2012 Papers

• Analyzing Survival Data with Competing Risks Using SAS Software
  Lin, Guixian; So Ying; Johnston, Gordon; SAS Institute, Inc. 2012

  The SAS macro %CIF implements appropriate nonparametric methods for estimating cumulative incidence functions. The macro also implements Gray’s method (Gray 1988) for testing differences between these functions in multiple groups. This paper discusses these methods and illustrates the macro.

• Introducing the FMM Procedure for Finite Mixture Models
  Kessler, Dave; McDowell, Allen; SAS Institute, Inc. 2012

  This paper provides an overview of the capabilities of the FMM procedure and illustrates them with applications drawn from a variety of fields.

• Look Out: After SAS/STAT 9.3 Comes SAS/STAT 12.1!
  Stokes, Maura; Chen, Fang; Yuan, Yang C.; Cai, Weijie; SAS Institute, Inc. 2012

  This paper reviews highlights from earlier releases and describes highlights of SAS/STAT 12.1, slated for release during 2012.

• Tips and Strategies for Mixed Modeling with SAS/STAT Procedures
  Kiernan, Kathleen; Tao, Jill; Gibbs, Phil; SAS Institute, Inc. 2012

  This paper provides recommendations for circumventing memory problems and reducing execution times for your mixed modeling analyses. It also shows how the new HPMIXED procedure can be beneficial for certain situations, as with large sparse mixed models. Lastly, the paper focuses on the best way to interpret and address common notes, warnings, and error messages that can occur with the estimation of mixed models in SAS software.

• Using SAS and LaTeX to Create Documents with Reproducible Results
  Arnold, Tim; Kuhfeld, Warren F.; SAS Institute 2012

  Documentation for many SAS analytical products has long been created from a single-source system that embeds SAS code in LaTeX files and generates statistical results from those files. This system is now available to SAS users as an open-source package. This paper describes how to access and implement the package, and it illustrates typical usage with several examples.

2011 Papers

• CONTRAST and ESTIMATE Statements Made Easy: The LSMESTIMATE Statement
  Kiernan, Kathleen; Tobias, Randy; Gibbs, Phil; Tao, Jill; SAS Institute, Inc. 2011

  The CONTRAST and ESTIMATE statements enable a variety of custom hypothesis tests, but using these statements correctly is often challenging. The new LSMESTIMATE statement, available in ten procedures in SAS/STAT 9.22 software, greatly simplifies the use of these statements. This paper discusses the new features available with the LSMESTIMATE statement and demonstrates them with examples from actual user questions to the Statistical Procedures group in SAS Technical Support.

• Making Use of Incomplete Observations in the Analysis of Structural Equation Models: The CALIS Procedure's Full Information Maximum Likelihood Method in SAS/STAT 9.3
  Yung, Yiu-Fai; Zhang, Wei; SAS Institute 2011

  This paper illustrates how you can use PROC CALIS to deal with random missing values in the following data-analytic situations: (1) estimating means and covariances, (2) regression analysis, and (3) structural equation or path modeling. This paper also illustrates some new features of PROC CALIS for analyzing missing patterns and data coverages.

• On Deck: SAS/STAT 9.3
  Stokes, Maura; Chen, Fang; So, Ying; SAS Institute 2011

  This paper reviews the highlights of SAS/STAT 9.22 and then describes important SAS/STAT 9.3 enhancements with practical illustrations, mainly from the SAS/STAT 9.3 documentation.

• The RANDOM Statement and More: Moving On with PROC MCMC
  Chen, Fang; SAS Institute 2011

  This paper describes key enhancements of PROC MCMC in SAS/STAT 9.22 and 9.3 and illustrates the use of these enhancements with examples.

• Small Area Estimation for Survey Data Analysis Using SAS Software
  Mukhopadhyay, Pushpal K.; McDowell, Allen; SAS Institute 2011

  This paper describes the use of the MIXED, IML, and MCMC procedures to fit unit-level and area-level models, and to obtain small area predictions and the mean squared error of predictions. Hierarchical Bayes models are also discussed as extensions to the basic models.

• A Tutorial on Structural Equation Modeling With Incomplete Observations: Multiple Imputation and FIML Methods Using SAS
  Zhang, Wei; Yung, Yiu-Fai; SAS Institute 2011

  This paper demonstrates the use of the MI, MIANALYSIS, and CALIS procedures of SAS/STAT (version 9.22 or later) to fit structural equation models with incomplete observations (or missing data).

• Up To Speed With Categorical Data Analysis
  Stokes, Maura, SAS Institute, Inc.; Koch, Gary, University of North Carolina; 2011

  This paper reviews some of the basic tenets of categorical data analysis today and describes the newer techniques that have become common practice.

2010 Papers

• Analyzing Interval-Censored Survival Data with SAS Software
  So, Ying; Johnston, Gordon; SAS Institute 2010; Kim, See Hee; UNC-Chapel Hill

  Interval-censored survival data comes from event time that is not directly observed and the event is known only to have occurred within some interval of time. The focus of this paper is how to use SAS software to analyze this type of data.

• Everything In Its Place: Efficient Geostatistical Analysis with SAS/STAT Spatial Procedures
  Kolovos, Alexander; SAS Institute 2010

  The paper demonstrates new features to the SAS suite of spatial procedures to augment, simplify, and streamline spatial analysis in one and two dimensions and discusses key elements and underlying complexities of stochastic spatial analysis.

• Introducing PROC PLM and Postfitting Analysis for Very General Linear Models in SAS/STAT 9.22
  Tobias, Randy; Cai, Weijie; SAS Institute 2010

  This paper discusses how the PLM procedure, new in SAS/STAT 9.22, works in conjunction with a new STORE statement in many familiar SAS/STAT procedures to provide a full complement of postprocessing features for a wide spectrum of linear models. The paper also discusses the new EFFECT statement, newly available in a number of procedures in SAS/STAT 9.22, which makes it easy for you to add many more types of effects to your general linear models—splines, polynomials, lags, and more.

• Introduction to Structural Equation Modeling Using the CALIS Procedure in SAS/STAT Software
  Yung, Yiu-Fai; SAS Institute 2010

  This paper discusses the following how-to techniques of the CALIS procedure (SAS/STAT 9.22) are covered: (1) Specifying structural equation models with latent variables by using the PATH modeling language; (2) Interpreting the model fit statistics and estimation results; (3) Testing models with multiple groups and multiple models; (4) Analyzing direct and indirect effects; (5) Modifying structural equation models.

• The Next Generation: SAS/STAT 9.22
  Stokes, Maura; Rodriguez, Robert; Cohen, Robert; SAS Institute 2010

  This paper provides an overview of the exciting new enhancements to SAS/STAT 9.22.

• Not Hazardous to Your Health: Proportional Hazards Modeling for Survey Data with the SURVEYPHREG Procedure
  Mukhopadhyay, Pushpal K.; SAS Institute 2010

  This paper introduces the SURVEYPHREG procedure, a new SAS/STAT procedure for finite population inference for the Cox proportional hazards model for complex surveys.

• An Overview of the CLASS, CONTRAST, and HAZARDRATIO Statements in the SAS 9.2 PHREG Procedure
  Savarese, Paul T; Patetta, Michael J.; SAS Institute 2010

  The paper concentrates on common CLASS variable parameterization methods such as reference coding and GLM coding. Caveats regarding CLASS variables and time (including time-dependent covariates) are also discussed.

2009 Papers

• All the Cows in Canada: Massive Mixed Modeling with the HPMIXED Procedure in SAS® 9.2
  Wang, Tianlin; SAS Institute 2009

  This paper introduces applications of large mixed models, discusses the specialized techniques of the HPMIXED procedure to handle them, and demonstrates the utility of the procedure with examples from agriculture and genomics.

• Applications of the GLMSELECT Procedure for Megamodel Selection
  Cohen, Robert; SAS Institute 2009

  This paper explores applications of the GLMSELECT procedure in SAS/STAT software and discusses the modeling possibilities available when you can select regression models from tens of thousands of effects.

• Bayesian Modeling Using the MCMC Procedure
  Chen, Fang; SAS Institute 2009

  This paper introduces the new MCMC procedure in SAS/STAT 9.2, which is designed for general-purpose Bayesian computations. It also describes how to use the MCMC procedure for estimation, inference, and prediction.

• Group Sequential Analysis Using the New SEQDESIGN and SEQTEST Procedures
  Yuan, Yang C.; SAS Institute 2009

  This paper reviews basic concepts of group sequential analysis and introduces two SAS/STAT procedures: the SEQDESIGN and SEQTEST procedures.

• Performing Exact Logistic Regression with the SAS System—Revised 2009
  Derr, Robert; SUGI Proceedings, Revised 2009

  This paper discusses the theory and methods for exact logistic regression and illustrates their application with the LOGISTIC procedure in SAS/STAT 9.2 software.

2008 Papers

• Fitting Generalized Additive Models with the GAM Procedure in SAS 9.2
  Cai, Weijie; SAS Institute, 2008

  This paper discusses an example of fitting generalized additive models with the GAM procedure, which provides multiple types of smoothers with automatic selection of smoothing parameters. This paper uses the ODS Statistical Graphics to produce plots of integrated additive and smoothing components.

• Try, Try Again: Replication-Based Variance Estimation Methods for Survey Data Analysis in SAS 9.2
  An, Anthony; Mukhopadhyay, Pushpal K.; Tobias, Randall; Watts, Donna L.; SAS Institute, 2008

  This paper discusses replication methods, comparing them to the Taylor series expansion method with respect to both technical characteristics and practical utility. This paper also discusses other significant enhancements to the survey design and analysis procedures in SAS 9.2.

• Updates to SAS Power and Sample Size Software in SAS 9.2
  Watson, Wayne E.; SAS Institute, 2008

  This paper highlights the new features of the power analysis and sample size determination procedures and describes the new desktop application.

2007 Papers

• Growing Up Fast: SAS 9.2 Enhancements to the GLIMMIX Procedure
  Schabenberger, Oliver; SAS Institute, 2007

  This paper describes the key changes and enhancements to PROC GLIMMIX between the SAS 9.1 and SAS 9.2 releases.

2006 Papers

• Introducing the GLMSELECT PROCEDURE for Model Selection
  Cohen, Robert A.; SAS Institute, 2006

  This paper describes the GLMSELECT procedure, a new procedure in SAS/STAT software that performs model selection in the framework of general linear models.

• Like Wine, the TTEST Procedure Improves with Age
  Castelloe, John; Tobias, Randy; SAS Institute, 2006

  This paper describes the new features of PROC TTEST in SAS 9.2 and illustrates their use in a number of examples.

• You Can't Stop Statistics: SAS/STAT Software Keeps Rolling Along
  Stokes, Maura; Rodriguez, Robert; R & D Staff; SAS Institute, 2006

  This paper describes the statistical development that will surface in SAS 9.2. First, the "post" SAS 9.1 download procedures will be reviewed, with examples from the GLIMMIX and GLMSELECT procedures. Attention turns next to a preview of SAS Stat Studio, the successor to SAS/INSIGHT software. ODS Graphics becomes production with SAS 9.2, and this paper reviews its many enhancements and describes the use of the new effect plot in PROC LOGISTIC.

2005 Papers

• Growth Charts of Body Mass Index (BMI) with Quantile Regression
  Chen, Colin (Lin); Proceedings of International Conference on Algorithmic Mathematics and Computer Science, Las Vegas, 2005

  Growth charts of body mass index (BMI) are constructed from the recent four-year national cross-sectional survey data (1999-2002) using parametric quantile regression methods, which are implemented with a newly developed SAS procedure and SAS macros.

• Introducing the GLIMMIX Procedure for Generalized Linear Mixed Models
  Schabenberger, Oliver; SAS Institute, 2005

  This paper describes a new SAS/STAT procedure for fitting models to non-normal or normal data with correlations or nonconstant variability. The GLIMMIX procedure is an add-on for the SAS/STAT product in SAS 9.1 on the Windows platform.

• An Introduction to Quantile Regression and the QUANTREG Procedure
  Chen, Colin (Lin); SAS Institute, 2005

  This paper describes the new QUANTREG procedure in SAS 9.1, which computes estimates and related quantities for quantile regression by solving a modification of the least-squares criterion.

2004 Papers

• Mixed Model Influence Diagnostics
  Schabenberger, Oliver; SAS Institute, 2004

  This paper examines extensions of influence measures in linear mixed models and their implementation in the MIXED procedure.

2003 Papers

• Analysis of Data from Recurrent Events
  Johnston, Gordon; So, Ying; SAS Institute, 2003

  This paper describes methods for the analysis of recurrent events data. Nonparametric methods involving extensive use of graphics for the analysis of such data are discussed in a new book by Nelson (2003). These methods are illustrated using the SAS/QC RELIABILITY procedure. The use of the SAS/STAT GENMOD and PHREG procedures to fit regression models to recurrent events data is also illustrated.

• Assessing the Numerical Accuracy of SAS Software
  Statistical R&D Staff, SAS Institute, 2003
  This paper provides an overview of the NIST tests, describes relevant SAS procedures for analyzing the NIST data sets, and summarizes the performance of the SAS System for these and other tests of numerical accuracy.

• Let the Data Speak: New Regression Diagnostics Based on Cumulative Residuals
  Johnston, Gordon; So, Ying; SAS Institute, 2003

  In this paper, new model-checking techniques of Lin et al. (1993, 2002) based on cumulative sums and other aggregates of residuals are described.

2002 Papers

• A Preview of SAS/STAT Version 9: Moving in New Directions and Building on Old Favorites
  Stokes, Maura; Rodriguez, Robert; Tobias, Randy; SAS Institute, 2002

  This paper outlines how Version 9 of SAS/STAT software brings you a variety of new tools for your statistical computing needs.

• Robust Regression and Outlier Detection with the ROBUSTREG Procedure
  Chen, Colin (Lin); SAS Institute, 2002

  This paper introduces the ROBUSTREG procedure, which is experimental in SAS/STAT Version 9. The ROBUSTREG procedure implements the most commonly used robust regression techniques

• SAS Meets Big Iron: High Performance Computing in SAS Anlaytic Procedures
  Cohen, Robert A.; SAS Institute, 2002

  This paper provides a survey of this development and the performance gains obtained in several procedures in SAS/STAT and Enterprise Miner.

2001 Papers

• The %Distribute System for Large-Scale Parallel Computation in the SAS System
  Doninger, Cheryl; Tobias, Randy; SAS Institute, 2001

  This paper describes the underlying approach used in %Distribute and demonstrates its application for Monte Carlo simulation.

• Fitting Generalized Additive Models with the GAM Procedure
  Xiang, Dong; SUGI Proceedings, 2001

  This paper describes the use of the GAM procedure for fitting generalized additive models (Hastie and Tibshirani, 1990).

• The Output Delivery System for Data Analysis
  Statistical R&D Staff, SAS Institute, 2001

  This powerpoint presentation has been given to numerous audiences as a two hour seminar and describes useful applications of ODS for statisticians and data analysts.

• Power and Sample Size Determination for Linear Models
  Castelloe, John M.; SAS Institute; O'Brien, Ralph G.; Cleveland Clinic Foundation, SUGI Proceedings, 2001.
  This presentation describes the steps involved in performing sample size analyses for a variety of linear models, both univariate and multivariate.

2000 Papers

• Estimating Directional Error Rates of Step-wise Multiple Comparisons Methods Using Distributed SAS Computing and Variance Reduction
  Westfall,Peter H.; Tobias, Randy; Bretz, Frank

  The purpose of this article is to systematically explore combined error rates (CER) of stepwise testing methods relevant to ANOVA studies involving correlated comparisons by using both analytic and simulation-based methods.

• Multiple Imputation for Missing Values: Concepts and New Development
  Yuan, Yang; SUGI Proceedings, 2000

  This paper reviews methods for analyzing missing data, including basic concepts and applications of multiple imputation techniques. The paper presents SAS procedures, PROC MI and PROC MIANALYZE, for creating multiple imputations for incomplete multivariate data and for analyzing results from multiply imputed data sets. It also describes new experimental features in Version 9.0 for specification of classification variables in the MI and MIANALYZE procedures.

• Repeated Measures with Zeroes
  Berk, Kenneth N.; Illinois State University and Peter A. Lachenbruch, FDA

  This paper compares two methods of modeling repeated measures data with many zeros. The first method models the nonzero observations as lognormal and the zeros are modeled as a mixture of Bernoulli and left-censored lognormal. The second method omits the censoring, leaving a logistic model for the zeros and a lognormal model for the nonzero values.

• Sample Size Computations and Power Analysis with the SAS System
  Castelloe, John; SUGI Proceedings, 2000

  This presentation demonstrates issues involved in power analysis, summarizes the current state of methodology and software, and outlines future directions.

• Selecting Chemical Libraries by Analogy with Statistical Experimental Design
  Tobias, Randy; SAS Institute, 2000

  Modern methods of combinatorial chemistry screen huge spaces of compounds for those which have the most potential as new drugs, new catalysts, and new materials. The task of identifying which compounds to test is formally quite similar to the task of selecting which factor combinations to observe in a statistical experimental design. This paper explores this similarity, demonstrating that computer-aided space-filling experimental design techniques may complement traditional, information-based approaches.

1999 and Earlier Papers

• Are Histograms Giving You Fits? New SAS Software for Analyzing Distributions
  Curtis, Nathan; WUSS Proceedings, 1999

  This presentation will introduce you to software for creating high-resolution graphics displays of data distributions, including histograms, probability plots, and quantile-quantile plots, which have been added to the UNIVARIATE procedure in Version 8.

• Fitting Nonlinear Mixed Models with the New NLMIXED Procedure
  Wolfinger, Russ; SUGI Proceedings, 1999

  This paper presents some of the primary features of PROC NLMIXED and illustrates its use with two examples.

• An Introduction to PROC LOESS for Local Regression
  Cohen, Robert; SUGI Proceedings, 1999

  This paper describes the LOESS procedure which is a new procedure in SAS/STAT software for performing local regression.

• Recent Advances in Categorical Data Analysis
  Stokes, Maura; SUGI Proceedings, 1999

  This paper describes software currently available in the SAS System and indicates the areas in which new software should be available in the next few years.

• Exploring ELISA Data with the Analyst Application
  Fossceco, Stewart; Knoll Pharmaceutical Company; Curtis, Nathan; SAS Institute, SUGI Proceedings, 1999

  This paper demonstrates how to use the SAS Analyst Application software's powerful statistical and graphical capabilities to investigate basic assay questions.

• ODS for Data Analysis: Output As-You-Like-It in Version 7
  Olinger, Christopher R.; Tobias, Randall D.; SUGI Proceedings, 1998

  This paper discusses advances made in the output generated by all Version 7 SAS Software procedures.

• New SAS Procedures for Analysis of Sample Survey Data
  An, Anthony; Watts, Donna; SUGI Proceedings, 1998

  This paper describes the capabilities of the SURVEYSELECT, SURVEYMEANS, and SURVEYREG procedures and illustrates their use.

• New Features in SAS/INSIGHT in Version 7
  Cohen, Marc-david; Chen, Hong; Yuan, Yang; Wicklin, Frederick; SUGI Proceedings, 1998.
  There are significant new features in Version 7 SAS/INSIGHT. These include new statistical analyses and enhancements to the graphics.

• Repeated Measures Analysis with Discrete Data Using the SAS System
  Johnston, Gordon; Stokes, Maura; SUGI Proceedings, 1996

  This paper provides an overview of the use of GEEs in the analysis of correlated data using the SAS System.

• Exact Methods in the NPAR1WAY Procedure
  Narayanan, Anupama; Watts, Donna; SUGI Proceedings, 1996

  This paper introduces the NPAR1WAY procedure's new exact statistics capabilities in SAS 6.11 software.

• Comparing the SAS GLM and MIXED Procedures for Repeated Measurements Analysis
  Wolfinger, Russ; Chang, Ming; SUGI Proceedings, 1995

  Repeated measures analyses in the GLM procedure involve the traditional univariate and multivariate approaches. The MIXED procedure employs a more general covariance structure approach. This paper compares the two procedures and helps you understand their methodologies

• A Tutorial on Logistic Regression
  So, Ying; SUGI Proceedings, 1995

  This tutorial answers some frequently asked questions about the LOGISTIC procedure.

• An Introduction to Partial Least Squares Regression
  Tobias, Randall D.; SUGI Proceedings, 1995

  The paper gives a brief overview of how PLS works, an extended chemometric example is presented that demonstrates how PLS models are evaluated and how their components are interpreted, and finally it discusses alternatives and extensions of PLS.

• SAS Software To Fit Generalized Linear Models
  Johnston, Gordon; SUGI Proceedings, 1993

  This paper introduces generalized linear models and reviews the SAS software that fits the models.

Papers are in Portable Document Format (PDF) and can be viewed with the free Adobe Acrobat Reader.

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Powerpoint presentations and SAS programs can be downloaded as zip files.