Abstract #300298

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JSM 2003 Abstract #300298
Activity Number: 225
Type: Invited
Date/Time: Tuesday, August 5, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #300298
Title: Bayesian Partition Model for Case-Control Studies involving Highly Polymorphic Candidate Genes
Author(s): Sylvia Richardson*+ and Shaun Seaman
Companies: Imperial School of Medicine and MRC Biostatistics Unit
Address: Department of Epidemiology & Public Health, Norfolk Place, London, W2 1PG,
Keywords: Case Control studies ; Bayesian Partition models ; Retrospective likelihood ; Highly Polymorphic Candidate Gene
Abstract:

The Bayesian framework for case-control studies offers possibilities for flexible hierarchical modeling that is needed in many applications. Here, we are concerned with case-control studies examining a highly polymorphic candidate disease-susceptibility gene, where many genotypes are possible and the number of subjects having each genotype is modest. Thus, if analyzed separately, the risks associated with most genotypes will be estimated imprecisely and there is a need to borrow information across the genotypes. The natural likelihood for case-control studies is the retrospective one. Recent approaches dealing with this likelihood in a Bayesian framework are computationally expensive (Müller and Roeder 1997, Müller et al 1999, Seaman and Richardson 2001). On the other hand, retrospective and prospective likelihoods are equivalent for ML. Thus, there is clear interest in developing conditions where this equivalence could hold in a Bayesian framework. We first describe conditions for this equivalence. We go on to present an application of Bayesian partition models for the analysis of case-control studies examining the risk associated with many different genotypes.

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