Generalized Estimating Equation

Abstract

The generalized estimating equation (GEE) uses a quasi-likelihood approach for analyzing data with correlated outcomes. This is an extension of GLM and uses quasi-likelihood method for cluster or repeated outcomes. If observations on outcome variable are repeated, it is likely that the observations are correlated. In addition, non-normality of outcome variables is a common phenomenon in real-life problems. In such situations, use of quasi-likelihood estimating equations provides necessary methodological support for estimating parameters of a regression model. The GEE is a marginal model approach for analyzing repeated measures data developed by Zeger and Liang (1986) and Liang and Zeger (1986). This approach can be considered as a semiparametric approach because it does not require full specification of the underlying joint probability distribution for repeated outcome variables rather assumes likelihood for marginal distribution and a working correlation matrix. The correlation matrix represents the correlation between observations in clusters observed from panel, longitudinal, or family studies. In this chapter, an overview of GEE is presented.