Effects of misspecification in the approach of generalized estimating equations for analysis of clustered data

Abstract

The GEE (Generalized Estimating Equation) approach is an estimation procedure based on the framework of Generalized Linear Model but incorporating within-subject correlation consideration. In general, the choice of the working correlation structure and variance function in GEE will affect the efficiency of estimation, and the effects of misspecification in correlation matrix and variance function are not well understood in the literature. In this thesis, three types of the misspecification are considered: (i) the incorrect choice of the correlation matrix structure; (ii) the discrepancy between different estimation methods of I?, the correlation parameter; (iii) the incorrect choice of variance function. Analytical results such as Asymptotic Relative Efficiency (ARE) are derived and simulation studies are carried out under different mis-specification conditions. An application to the cow data set is used for illustration.