%0 Journal Article
%A Liu, Gang
%A Mukherjee, Bhramar
%A Lee, Seunggeun
%A Lee, Alice W
%A Wu, Anna H
%A Bandera, Elisa V
%A Jensen, Allan
%A Rossing, Mary Anne
%A Moysich, Kirsten B
%A Chang-Claude, Jenny
%A Doherty, Jennifer A
%A Gentry-Maharaj, Aleksandra
%A Kiemeney, Lambertus
%A Gayther, Simon A
%A Modugno, Francesmary
%A Massuger, Leon
%A Goode, Ellen L
%A Fridley, Brooke L
%A Terry, Kathryn L
%A Cramer, Daniel W
%A Ramus, Susan J
%A Anton-Culver, Hoda
%A Ziogas, Argyrios
%A Tyrer, Jonathan P
%A Schildkraut, Joellen M
%A Kjaer, Susanne K
%A Webb, Penelope M
%A Ness, Roberta B
%A Menon, Usha
%A Berchuck, Andrew
%A Pharoah, Paul D
%A Risch, Harvey
%A Pearce, Celeste Leigh
%T Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.
%J American journal of epidemiology
%V 187
%N 2
%@ 1476-6256
%C Oxford
%I Oxford Univ. Press
%M DKFZ-2018-00182
%P 366 - 377
%D 2018
%X 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:28633381
%R 10.1093/aje/kwx243
%U https://inrepo02.dkfz.de/record/132495