Held on Thursday, June 3, 2010 at the Crowne Plaza Hotel in Northbrook, IL.
The 26th Annual Summer Workshop of the Northeastern Illinois Chapter of the American Statistical Association will be given on the topic of:
SAS for Mixed Models
presented by: Walt Stroup, Ph.D., Professor & Dept. Head of Statistics, University of Nebraska
The course is intended for those who want to learn about the theory and application of generalized linear mixed models from a non-Bayesian perspective. The material is presented at an applied level, accessible to participants with training in linear statistical models and previous exposure to linear mixed models. Examples are drawn from a wide variety of allied disciplines. We will begin with an overview of the main ideas of generalized linear mixed model theory. We make the connection between linear models, generalized linear models, linear mixed models, and generalized linear mixed models (GLMM) in terms of model formulation, distributional properties, and approaches to estimation and inference. The overview will include overarching issues that confront analysts who work with correlated, non-normal data, such as overdispersion, the marginal and conditional models, and model diagnostics. Examples will use SAS PROC GLIMMIX – accordingly, the introductory overview will include a brief look at GLIMMIX syntax. The main focus of the examples will be on a variety of issues involved in mixed model analysis and its extension to rate and proportion data and count data. The rate & proportion examples concentrate on binary, binomial and multinomial data in the presence of random model effects and/or repeated measures. The count data includes various distributions (e.g. Poisson and negative binomial), their modeling rationale and how to choose among them in practical situations. We also consider several zero-inflated models. The final section focuses on power analysis and planning. GLMM theory allows comparison of competing designs not possible with conventional power and sample size software. Most “conventional wisdom” about design assumes normally distributed data. This conventional wisdom is often inappropriate – sometimes catastrophically so – for GLMMs. The bottom line is that the modeling, analysis, and design aspects of GLMMs cannot be compartmentalized, but the design aspect has received relatively too little attention. This part of the course will illustrate tools that can be used to help plan studies to effectively take advantage of GLMM capabilities.
Walter W. Stroup, Ph.D. is Professor and Head of the Department of Statistics at the University of Nebraska, where he has been a faculty member since 1979. Dr. Stroup received a B.A. degree in psychology from Antioch College, and M.S. and Ph.D. degrees in statistics from the University of Kentucky. Dr. Stroup is coauthor of SAS for Mixed Models and SAS for Linear Models and he is widely published in statistical and applied journals. He has presented numerous short courses on mixed models, nonlinear mixed models, and generalized linear mixed models in academic, professional society, and industry based settings.