title: Marginal analyses of clustered data when cluster size is informative.
abstract: We propose a new approach to fitting marginal models to clustered data when cluster size is informative. This approach uses a generalized estimating equation (GEE) that is weighted inversely with the cluster size. We show that our approach is asymptotically equivalent to within-cluster resampling (Hoffman, Sen, and Weinberg, 2001, Biometrika 73, 13-22), a computationally intensive approach in which replicate data sets containing a randomly selected observation from each cluster are analyzed, and the resulting estimates averaged. Using simulated data and an example involving dental health, we show the superior performance of our approach compared to unweighted GEE, the equivalence of our approach with WCR for large sample sizes, and the superior performance of our approach compared with WCR when sample sizes are small.
authors.affiliation: Division of HIV/AIDS Prevention, National Center for HIV, STD and TB Prevention, Centers for Disease Control and Prevention, MS E-37, 1600 Clifton Road, NE, Atlanta, Georgia 30333, USA. jow5@cdc.gov
authors.forename: John M
authors.initials: JM
authors.lastname: Williamson
authors.forename: Somnath
authors.initials: S
authors.lastname: Datta
authors.forename: Glen A
authors.initials: GA
authors.lastname: Satten
mesh_terms: D016000:Cluster Analysis; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D010518:Periodontitis / Q000453:epidemiology; D018401:Sample Size