Informative cluster sizes for subcluster-level covariates and weighted generalized estimating equations.

ABSTRACT: Williamson, Datta, and Satten's (2003, Biometrics 59, 36-42) cluster-weighted generalized estimating equations (CWGEEs) are effective in adjusting for bias due to informative cluster sizes for cluster-level covariates. We show that CWGEE may not perform well, however, for covariates that can take different values within a cluster if the numbers of observations at each covariate level are informative. On the other hand, inverse probability of treatment weighting accounts for informative treatment propensity but not for informative cluster size. Motivated by evaluating the effect of a binary exposure in presence of such types of informativeness, we propose several weighted GEE estimators, with weights related to the size of a cluster as well as the distribution of the binary exposure within

SUBMITTER: Huang Y 

PROVIDER: S-EPMC3137732 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature