title: Small sample GEE estimation of regression parameters for longitudinal data.
abstract: Longitudinal (clustered) response data arise in many bio-statistical applications which, in general, cannot be assumed to be independent. Generalized estimating equation (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias adjusted GEE estimators of the regression parameters in longitudinal data are obtained when the number of subjects is small. One is based on a bias correction, and the other is based on a bias reduction. Simulations show that the performances of both the bias-corrected methods are similar in terms of bias, efficiency, coverage probability, average coverage length, impact of misspecification of correlation structure, and impact of cluster size on bias correction. Both these methods show superior properties over the GEE estimates for small samples. Further, analysis of data involving a small number of subjects also shows improvement in bias, MSE, standard error, and length of the confidence interval of the estimates by the two bias adjusted methods over the GEE estimates. For small to moderate sample sizes (N ≤50), either of the bias-corrected methods GEEBc and GEEBr can be used. However, the method GEEBc should be preferred over GEEBr, as the former is computationally easier. For large sample sizes, the GEE method can be used.
authors.affiliation: Department of Mathematics and Statistics, Trent University, Peterborough, Ontario, K9J7B8, Canada.
authors.forename: Sudhir
authors.initials: S
authors.lastname: Paul
authors.forename: Xuemao
authors.initials: X
authors.lastname: Zhang
mesh_terms: D015982:Bias; D016000:Cluster Analysis; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D011336:Probability; D018401:Sample Size
keywords: bias correction; bias reduction; generalized estimating equations; longitudinal data; marginal model