Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 176, Issue 11, Pages 1060-1067Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kws166
Keywords
additive risk model; case-control studies; gene-environment independence; gene-environment interaction; multiplicative risk model
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Funding
- National Institutes of Health, National Cancer Institute (Division of Cancer Epidemiology and Genetics)
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There has been a long-standing controversy in epidemiology with regard to an appropriate risk scale for testing interactions between genes (G) and environmental exposure (E). Although interaction tests based on the logistic modelwhich approximates the multiplicative risk for rare diseaseshave been more widely applied because of its convenience in statistical modeling, interactions under additive risk models have been regarded as closer to true biologic interactions and more useful in intervention-related decision-making processes in public health. It has been well known that exploiting a natural assumption of G-E independence for the underlying population can dramatically increase statistical power for detecting multiplicative interactions in case-control studies. However, the implication of the independence assumption for tests for additive interaction has not been previously investigated. In this article, the authors develop a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study.
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