title: Effects of variance-function misspecification in analysis of longitudinal data.
abstract: The approach of generalized estimating equations (GEE) is based on the framework of generalized linear models but allows for specification of a working matrix for modeling within-subject correlations. The variance is often assumed to be a known function of the mean. This article investigates the impacts of misspecifying the variance function on estimators of the mean parameters for quantitative responses. Our numerical studies indicate that (1) correct specification of the variance function can improve the estimation efficiency even if the correlation structure is misspecified; (2) misspecification of the variance function impacts much more on estimators for within-cluster covariates than for cluster-level covariates; and (3) if the variance function is misspecified, correct choice of the correlation structure may not necessarily improve estimation efficiency. We illustrate impacts of different variance functions using a real data set from cow growth.
authors: Department of Statistics and Applied Probability, National University of Singapore, 3 Science Drive 2, Singapore 117546. stawyg@nus.edu.sg
authors: You-Gan
authors: YG
authors: Wang
authors: Xu
authors: X
authors: Lin
mesh_terms: D000818:Animals; D001699:Biometry / Q000379:methods*; D002417:Cattle; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D016011:Normal Distribution; D012044:Regression Analysis