期刊
BIOMETRICS
卷 66, 期 3, 页码 934-948出版社
WILEY
DOI: 10.1111/j.1541-0420.2009.01357.x
关键词
Case-only design; Gene-environment independence; Highest posterior density interval; Molecular epidemiology of colorectal cancer; Multinomial-Dirichlet; Posterior odds
资金
- NSF [DMS 07-06935, SES-063426]
- NIH [R03 CA130045-01, R01 CA81488]
- National Heart Lung and Blood Institute [R01 HL091172-01]
- National Cancer Institute
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1007417] Funding Source: National Science Foundation
P>With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene-environment interaction. In many well-studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial prior information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene-environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene-environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene-environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case-control study of colorectal cancer investigating the interaction of N-acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.
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