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

Research output: Contribution to journalArticle

4 Citations (Scopus)

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

Fingerprint

Bayes Estimate
Empirical Bayes
Estimator
Population
Chronic Disease
Empirical Bayes Estimator
Costs and Cost Analysis
Research
Adjustment
Regression
Unstable
Simulation Study
Subgroup
Necessary
Community
Costs
Estimate
Design

ASJC Scopus subject areas

  • Epidemiology

Cite this

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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. Copyright (C) 2000 John Wiley and Sons, Ltd.",
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