Parametric empirical Bayes estimates of disease prevalence using stratifedsamples from community populations

Studies of chronic diseases in a community setting often employ stratified sample designs to enable the study to attain multiple research goals at a r easonable cost. One important goal is estimation of disease prevalence in t he whole community and in important subgroups. Some adjustment for the samp le design is necessary; if the design has many strata with very disparate s ampling fractions, simply upweighting observed stratum prevalences may lead to unstable estimators. We propose a parametric empirical Bayes estimator in the spirit of the work of Efron and Morris, and we compare it to the dir ect upweighted estimator and a regression-smoothed estimator. Simulation st udies in realistic settings suggest that the new estimator performs best, g iving estimates with low bias and good precision under a variety of models. Copyright (C) 2000 John Wiley & Sons, Ltd.