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

Beckett, LA and DJ Tancredi

Stat. Med.. 2000. 19(5):681-695.

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 & Sons, Ltd.

Keywords: parkinsonism; AD; longitudinal study, Extrapyramidal Signs; Elderly Individuals; Predictors; Dementia; Performance; Mortality; Psychosis; Diagnosis; Decline; Cohort

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