TY  - JOUR
AU  - Liu, Gang
AU  - Mukherjee, Bhramar
AU  - Lee, Seunggeun
AU  - Lee, Alice W
AU  - Wu, Anna H
AU  - Bandera, Elisa V
AU  - Jensen, Allan
AU  - Rossing, Mary Anne
AU  - Moysich, Kirsten B
AU  - Chang-Claude, Jenny
AU  - Doherty, Jennifer A
AU  - Gentry-Maharaj, Aleksandra
AU  - Kiemeney, Lambertus
AU  - Gayther, Simon A
AU  - Modugno, Francesmary
AU  - Massuger, Leon
AU  - Goode, Ellen L
AU  - Fridley, Brooke L
AU  - Terry, Kathryn L
AU  - Cramer, Daniel W
AU  - Ramus, Susan J
AU  - Anton-Culver, Hoda
AU  - Ziogas, Argyrios
AU  - Tyrer, Jonathan P
AU  - Schildkraut, Joellen M
AU  - Kjaer, Susanne K
AU  - Webb, Penelope M
AU  - Ness, Roberta B
AU  - Menon, Usha
AU  - Berchuck, Andrew
AU  - Pharoah, Paul D
AU  - Risch, Harvey
AU  - Pearce, Celeste Leigh
TI  - Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.
JO  - American journal of epidemiology
VL  - 187
IS  - 2
SN  - 1476-6256
CY  - Oxford
PB  - Oxford Univ. Press
M1  - DKFZ-2018-00182
SP  - 366 - 377
PY  - 2018
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:28633381
DO  - DOI:10.1093/aje/kwx243
UR  - https://inrepo02.dkfz.de/record/132495
ER  -