Analysis of variance for repeated measures data: A generalized estimating equations approach

Abstract

Various techniques are available for the analysis of repeated measures data, and the appropriate choice depends on distributional assumptions and study design features. A correct analysis must account for potential dependence between repeated observations on the same subject. Liang and Zeger proposed a more unified approach to the analysis of repeated measures data based on the application of generalized estimating equations. We examine the application of these methods to several types of data in which one estimates the mean response directly for each combination of discrete covariates, and uses an identity link. Computations for fitting this type of model are exceptionally simple. Numerical examples suggest that the proposed approach yields estimation and hypothesis testing results consistent with more specialized methods.