Regression analysis of correlated binary outcomes

Abstract

The purpose of this paper is to describe and illustrate a regression approach to the analysis of correlated binary outcomes (Liang & Zeger, 1986). Ignoring the correlations between repeated observations can lead to invalid inferences. This approach extends logistic regression to account for repeated observations in each of a series of individuals. In this paper, I present a nontechnical introduction to the generalized estimating equations (GEE) approach. A fictitious example is used to demonstrate that GEE regression correctly adjusts for the correlations between repeated binary observations. The approach is illustrated with an analysis of safer sex practices among high-risk teenagers.