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Semiparametric Bayesian Analysis of Case–Control Data under Conditional Gene‐Environment Independence
Author(s) -
Mukherjee Bhramar,
Zhang Li,
Ghosh Malay,
Sinha Samiran
Publication year - 2007
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2007.00750.x
Subject(s) - covariate , conditional independence , econometrics , semiparametric regression , population , population stratification , bayesian probability , semiparametric model , independence (probability theory) , logistic regression , nonparametric statistics , computer science , statistics , mathematics , biology , medicine , genetics , environmental health , genotype , single nucleotide polymorphism , gene
Summary 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, Biometrika 92, 399–418). However, covariates that stratify the population, such as age, ethnicity and alike, could potentially lead to nonindependence. In this article, we provide a novel semiparametric Bayesian approach to model stratification effects 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 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 model assumptions do not hold.