Statistical Laboratory
Iowa State University
DATE AND TIME: Monday, February 20, 2006, 4:10 p.m.
PLACE: 319 Snedecor
SPEAKER: Samiran Sinha, Department of Statistics, Texas A & M
University, College Station, Texas
TITLE: Semiparametric Bayesian Analysis of Case-Control Data under
Gene-Environment Independence and Population Stratification
ABSTRACT
In case-control studies of gene-environment association with disease, when
genetic and environmental exposures can be assumed to be independent in the
underlying population, one may exploit the independence in order to derive more
e cient estimation techniques than the traditional logistic regression analysis
(Chatterjee and Carroll, 2005). In this article, we provide a novel
semiparametric Bayesian approach to model strati cation e ects under the
assumption of gene-environment independence in the control population. We
illustrate the methods by applying them to data from a population-based
case-control study on ovarian cancer conducted in Israel. A simulation study is
conducted to compare our method with other popular choices. The results re ect
that the semiparametric Bayesian model allows incorporation of key scienti c
evidence in the form of a prior and o ers a exible, robust alternative when
standard parametric model assumptions do not hold.
Key words: Dirichlet process prior; Exponential family; Gene-environment
interaction; Logistic regression; Ovarian cancer; Population strati cation;
Zero-in ated.
COFFEE: 3:45 p.m., 104 Snedecor Hall
Jeanette La Grange
Department of Statistics
102 Snedecor
Iowa State University
Ames, IA 50010-1210
515 294-3440 (office)
515 294-4040 (fax)