Review of Software to Fit Generalized Estimating Equation Regression Models

Abstract Researchers are often interested in analyzing data that arise from a longitudinal or clustered design. Although there are a variety of standard likelihood-based approaches to analysis when the outcome variables are approximately multivariate normal, models for discrete-type outcomes generally require a different approach. Liang and Zeger formalized an approach to this problem using generalized estimating equations (GEEs) to extend generalized linear models (GLMs) to a regression setting with correlated observations within subjects. In this article, we briefly review GLM, the GEE methodology, introduce some examples, and compare the GEE implementations of several general purpose statistical packages (SAS, Stata, SUDAAN, and S-Plus). We focus on the user interface, accuracy, and completeness of implementations of this methodology.