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) |
|---|---|
| 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 |
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ASJC Scopus subject areas
- Epidemiology
Cite this
Parametric empirical Bayes estimates of disease prevalence using stratified samples from community populations. / Beckett, Laurel A; Tancredi, Daniel J.
In: Statistics in Medicine, Vol. 19, No. 5, 15.03.2000, p. 681-695.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Parametric empirical Bayes estimates of disease prevalence using stratified samples from community populations
AU - Beckett, Laurel A
AU - Tancredi, Daniel J
PY - 2000/3/15
Y1 - 2000/3/15
N2 - 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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=0034654492&partnerID=8YFLogxK
U2 - 10.1002/(SICI)1097-0258(20000315)19:5<681::AID-SIM343>3.0.CO;2-Y
DO - 10.1002/(SICI)1097-0258(20000315)19:5<681::AID-SIM343>3.0.CO;2-Y
M3 - Article
C2 - 10700739
AN - SCOPUS:0034654492
VL - 19
SP - 681
EP - 695
JO - Statistics in Medicine
JF - Statistics in Medicine
SN - 0277-6715
IS - 5
ER -