Parametric empirical Bayes estimates of disease prevalence using stratified samples from community populations |
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Authors: | Beckett L A Tancredi D J |
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Institution: | Rush Institute for Healthy Aging, Rush-Presbyterian-St. Luke's Medical Center, 1645 W. Jackson, Suite 675, Chicago, IL 60612, USA. lbeckett@crha.rpslmc.edu |
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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. |
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