Parametric empirical Bayes estimates of disease prevalence using stratified samples 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 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.
Beckett
L A
LA
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
Tancredi
D J
DJ
D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000704:Analysis of Variance; D001499:Bayes Theorem; D002908:Chronic Disease / Q000453:epidemiology*; D016001:Confidence Intervals; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D015233:Models, Statistical; D012107:Research Design; D012494:Sampling Studies; D012737:Sex Factors