Bayesian semiparametric analysis of gene-environment interactionn unde conditional gene-environment independence (Venue: GH seminar RM2)
Seminar Room 2, Newton Institute Gatehouse
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 efficient estimation techniques than the traditional logistic regression analysis (Chatterjee and Carroll, 2005). However, covariates that stratify the population, such as age,ethnicity and alike, could potentially lead to non-independence. Modeling these stratification effects introduce a large number of parameters in the retrospective likelihood. We provide a novel semiparametric Bayesian approach to model stratification effects under the assumption of gene-environment independence in the control population using a Dirichlet Process Mixture. 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 reflect that the semiparametric Bayesian model allows incorporation of key scientific evidence in the form of a prior and offers a flexible, robust alternative when standard parametric modelassumptions for the distribution of the genetic and environmental exposures do not hold.
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