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Parametric empirical Bayes estimates of disease prevalence using stratifed samples from community populations
Author(s) -
Beckett Laurel A.,
Tancredi Daniel J.
Publication year - 2000
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(20000315)19:5<681::aid-sim343>3.0.co;2-y
Subject(s) - bayes' theorem , statistics , parametric statistics , econometrics , computer science , bayesian probability , mathematics
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 © 2000 John Wiley & Sons, Ltd.