JSM 2005 Online Program

Abstract #303106

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Activity Number:	178
Type:	Topic Contributed
Date/Time:	Monday, August 8, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303106
Title:	Bayesian Inference for Matched Case-Control Studies
Author(s):	Samiran Sinha*+
Companies:	Texas A&M University
Address:	Department of Statistics, College Station, TX, 77843, United States
Keywords:	Logistic regression ; Missing at random ; Dirichlet process prior ; Gibbs sampler
Abstract:	Case-control study is the simplest of retrospective study design. This design is used widely to study association between a disease and its potential risk factor. We focus on the situation when the risk factor is partially missing for study units in a highly stratified case-control study, termed as matched case-control study. To deal with missing observations, we assume a parametric distribution for the exposure variable in the control population. The inference on all parameters is made through joint likelihood function of the disease and exposure variable and by efficient use of Markov chain Monte Carlo technique. The novelty of the proposed method is how we handle the unmeasured stratification effect in the distribution of the exposure variable by using a nonparametric Bayesian approach. Several examples and a simulation study are presented in support of the proposed method. We consider extensions of the proposed method to the situations when the disease has multiple categories and when the exposure variable is measured with errors.

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