Carcinogen metabolism, cigarette smoking, and breast cancer risk: a Bayes model averaging approach.

ABSTRACT:

Background

Standard logistic regression with or without stepwise selection has the disadvantage of not incorporating model uncertainty and the dependency of estimates on the underlying model into the final inference. We explore the use of a Bayes Model Averaging approach as an alternative to analyze the influence of genetic variants, environmental effects and their interactions on disease.

Methods

Logistic regression with and without stepwise selection and Bayes Model Averaging were applied to a population-based case-control study exploring the association of genetic variants in tobacco smoke-related carcinogen pathways with breast cancer.

Results

Both regression and Bayes Model Averaging highlighted a significant effect of NAT1*10 on breast cancer, while regression a

SUBMITTER: Stephenson N 

PROVIDER: S-EPMC2999590 | biostudies-literature | 2010 Nov

REPOSITORIES: biostudies-literature