The Pittsburgh Chapter of the ASA is sponsoring a short course in Applied Mixed Models on Monday October 8, hosted by the Biostatistics Department at the University of Pittsburgh, taught by Prof. Linda J. Young of the University of Florida. The cost is $65 for regular Chapter members, $50 for students, and $75 for non-student non-members (though this includes a Chapter membership!)
Data sets from designed experiments, sample surveys, and observational studies often contain correlated observations due to random effects and repeated measures. Mixed models can be used to accommodate the correlation structure, produce efficient estimates of means and differences between means, and provide valid estimates of standard errors. Repeated measures and longitudinal data require special attention because they involve correlated data that arise when the primary sampling units are measured repeatedly over time or under different conditions. Normal theory models for random effects and repeated measures ANOVA will be used to introduce the concept of correlated data. These models are then extended to generalized linear mixed models for the analysis of non-normal data, including binomial responses, Poisson counts, and over-dispersed count data. Methods of assessing the fit and deciding among competing models will be discussed. Accounting for spatial correlation and radial smoothing splines within mixed models will be presented and their application illustrated. The use of SAS System’s PROC GLIMMIX will be introduced as an extension of PROC MIXED and used to analyze data from pharmaceutical trials, environmental studies, educational research, and laboratory experiments.