Exposure Enriched Case‐Control (EECC) Design for the Assessment of Gene–Environment Interaction |
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| Authors: | Md Hamidul Huque Raymond J. Carroll Nancy Diao David C. Christiani Louise M. Ryan |
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| Affiliation: | 1. School of Mathematical and Physical Sciences, University of Technology Sydney, New South Wales, Australia;2. Department of Statistics, Texas A&M University, College Station, Texas, United States of American;3. Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, United States of American;4. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, United States of American |
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| Abstract: | Genetic susceptibility and environmental exposure both play an important role in the aetiology of many diseases. Case‐control studies are often the first choice to explore the joint influence of genetic and environmental factors on the risk of developing a rare disease. In practice, however, such studies may have limited power, especially when susceptibility genes are rare and exposure distributions are highly skewed. We propose a variant of the classical case‐control study, the exposure enriched case‐control (EECC) design, where not only cases, but also high (or low) exposed individuals are oversampled, depending on the skewness of the exposure distribution. Of course, a traditional logistic regression model is no longer valid and results in biased parameter estimation. We show that addition of a simple covariate to the regression model removes this bias and yields reliable estimates of main and interaction effects of interest. We also discuss optimal design, showing that judicious oversampling of high/low exposed individuals can boost study power considerably. We illustrate our results using data from a study involving arsenic exposure and detoxification genes in Bangladesh. |
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| Keywords: | Arsenic exposure case‐control gene– environment logistic regression power |
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