Abstract
There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
Original language | English (US) |
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Pages (from-to) | 366-377 |
Number of pages | 12 |
Journal | American journal of epidemiology |
Volume | 187 |
Issue number | 2 |
DOIs | |
State | Published - Feb 1 2018 |
Keywords
- bias-variance tradeoff
- effect modification
- empirical Bayes estimation
- genetic risk score
- relative excess risk
- shrinkage
ASJC Scopus subject areas
- General Medicine