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 language | English (US) |
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Pages (from-to) | 681-695 |
Number of pages | 15 |
Journal | Statistics in Medicine |
Volume | 19 |
Issue number | 5 |
DOIs | |
State | Published - Mar 15 2000 |
Externally published | Yes |
ASJC Scopus subject areas
- Epidemiology