This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 341
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #307914
Title: A Bayesian Method for Estimating Disease Prevalence in the Presence of a Hidden Subpopulation
Author(s): Chaoxiong Xia*+ and Paul Gustafson
Companies: The University of British Columbia and The University of British Columbia
Address: 333-6356 Agricultural Road, Vancouver, BC, V6T 1Z2, Canada
Keywords: Disease Prevalence ; Bayesian Inference ; Markov Chain Monte Carlo ; Nonidentified Models ; Survey Sampling ; Weighted Sampling
Abstract:

When estimating disease prevalence in a population, the presence of a hidden sub-population that cannot be sampled will lead to nonidentifiability and potentially biased estimation. This is a particular concern if the sub-population has different behavior in terms of disease risk factors. We propose a Bayesian model of disease prevalence for a weighted sample from the non-hidden portion of the population, by modeling the relationship between prevalence and sampling weight. The behavior of the posterior distribution on prevalence is studied through the posterior mean and variance. Both analytical forms and Markov chain Monte Carlo estimation of the limiting posteriors are presented to evaluate the effectiveness of statistical learning. Our work confirms that sensible results can be obtained with Bayesian analysis, despite the nonidentifiability in this situation.


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