Journal Article

DKFZ-2018-00182

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.

Liu, G. ; Mukherjee, B. ; Lee, S. ; Lee, A. W. ; Wu, A. H. ; Bandera, E. V. ; Jensen, A. ; Rossing, M. A. ; Moysich, K. B. ; Chang-Claude, J.DKFZ* ; Doherty, J. A. ; Gentry-Maharaj, A. ; Kiemeney, L. ; Gayther, S. A. ; Modugno, F. ; Massuger, L. ; Goode, E. L. ; Fridley, B. L. ; Terry, K. L. ; Cramer, D. W. ; Ramus, S. J. ; Anton-Culver, H. ; Ziogas, A. ; Tyrer, J. P. ; Schildkraut, J. M. ; Kjaer, S. K. ; Webb, P. M. ; Ness, R. B. ; Menon, U. ; Berchuck, A. ; Pharoah, P. D. ; Risch, H. ; Pearce, C. L. ; Consortium, O. C. A. (Collaboration Author)

2018
Oxford Univ. Press Oxford

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Please use a persistent id in citations: doi:10.1093/aje/kwx243

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.

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Contributing Institute(s):

1. Epidemiologie von Krebserkrankungen (C020)

Research Program(s):

1. 313 - Cancer risk factors and prevention (POF3-313) (POF3-313)

Appears in the scientific report 2018

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Database coverage:
; Allianz-Lizenz / DFG ; BIOSIS Previews ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; IF >= 5 ; JCR ; NCBI Molecular Biology Database ; Nationallizenz ; SCOPUS ; Science Citation Index ; Science Citation Index Expanded ; Thomson Reuters Master Journal List ; Web of Science Core Collection

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