“Introducing the GLIMMIX Procedure for Generalized Linear Mixed Models"

May 8, 2007
A Presentation by Maura Stokes


This paper describes a new SAS/STAT procedure for fitting models to non-normal or normal data with correlations or nonconstant variability. PROC GLIMMIX extends the SAS mixed model tools in a number of ways. For example, it

* models data from non-Gaussian distributions
* implements low-rank smoothing based on mixed models
* provides new features for LS-means comparisons and display
* enables you to use SAS programming statements to compute model effects, or to define link and variance functions
* fits models to multivariate data in which observations do not all have the same distribution or link

Applications of the GLIMMIX procedure include estimating trends in disease rates, modeling counts or proportions over time in a clinical trial, predicting probability of occurrence in time series and spatial data, and joint modeling of correlated binary and continuous data.

This presentation described generalized linear mixed models and how to use the GLIMMIX procedure for estimation, inference, and prediction. Examples from several application areas were presented.

Maura Stokes is an R & D Director in the statistical software development division at SAS. She is the lead author of the book Categorical Data Analysis Using the SAS System, and she has taught and written about categorical data analysis for over twenty years.

 
Maura Stokes 1

Cecilia Yee giving Maura Stokes a Certificate of Appreciation from the Detroit Chapter

 Maura Stokes2