Parametric empirical Bayes estimates of disease prevalence using stratified samples from community populations

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Studies of chronic diseases in a community setting often employ stratified sample designs to enable the study to attain multiple research goals at a reasonable cost. One important goal is estimation of disease prevalence in the whole community and in important subgroups. Some adjustment for the sample design is necessary; if the design has many strata with very disparate sampling 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 direct upweighted estimator and a regression-smoothed estimator. Simulation studies in realistic settings suggest that the new estimator performs best, giving estimates with low bias and good precision under a variety of models. Copyright (C) 2000 John Wiley and Sons, Ltd.

Original languageEnglish (US)
Pages (from-to)681-695
Number of pages15
JournalStatistics in Medicine
Volume19
Issue number5
DOIs
StatePublished - Mar 15 2000
Externally publishedYes

ASJC Scopus subject areas

  • Epidemiology

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