Online Program Home
My Program

Abstract Details

Activity Number: 40
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: Survey Research Methods Section
Abstract #319784
Title: Using a Power Prior to Improve County-Level Diabetes Incidence Estimation
Author(s): Hui Xie* and Deborah Rolka
Companies: CDC and CDC
Keywords: Small Area Estimation (SAE) ; Power Prior ; Bayesian Hierarchical Model ; County-Level ; Diabetes Incidence
Abstract:

It is nearly impossible to directly estimate diabetes incidence rates for most US counties due to the small number of individuals surveyed and the very low fraction of new cases observed in each county. Small area estimation (SAE) methods that use Bayesian hierarchical models help address this problem by borrowing strength from other counties and states. Still, the accuracy of the incidence estimates is limited by the lack of data; some counties have no survey data whatsoever. Using SAE models, county incidence is estimated by drawing from the posterior distribution, which is comprised of prior and likelihood factors. In counties with few or no observations, the posterior draws are heavily influenced by the selected prior distribution. To improve our estimates, we implemented a Bayesian hierarchical SAE model with an informative (power) prior that adds relevant historical information. We used survey and simulated data to examine our proposed model. Compared with the diffuse prior, in terms of relative root mean squared error (RRMSE), the power prior can improve estimation accuracy by 21% when 40% of counties have zero observations, and by 27% when 60% of counties have no data.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2016 program

 
 
Copyright © American Statistical Association