Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence
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Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. / Liu, Gang; Lee, Seunggeun; Lee, Alice W; Wu, Anna H; Bandera, Elisa V; Jensen, Allan; Anne Rossing, Mary; Moysich, Kirsten B; Chang-Claude, Jenny; Doherty, Jennifer Anne; Gentry-Maharaj, Aleksandra; Kiemeney, Lambertus A; Gayther, Simon A; Modugno, Francesmary; Massuger, Leon F A G; Goode, Ellen L; Fridley, Brooke L; Terry, Kathryn L; Cramer, Daniel W; Ramus, Susan J; Anton-Culver, Hoda; Ziogas, Argyrios; Tyrer, Jonathan P; Schildkraut, Joellen M; Kjaer, Susanne Krüger; Webb, Penelope M; Ness, Roberta B; Menon, Usha; Berchuck, Andrew; Pharoah, Paul D; Risch, Harvey A; Leigh Pearce, Celeste; Mukherjee, Bhramar.
In: AM J EPIDEMIOL, 14.06.2017.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence
AU - Liu, Gang
AU - Lee, Seunggeun
AU - Lee, Alice W
AU - Wu, Anna H
AU - Bandera, Elisa V
AU - Jensen, Allan
AU - Anne Rossing, Mary
AU - Moysich, Kirsten B
AU - Chang-Claude, Jenny
AU - Doherty, Jennifer Anne
AU - Gentry-Maharaj, Aleksandra
AU - Kiemeney, Lambertus A
AU - Gayther, Simon A
AU - Modugno, Francesmary
AU - Massuger, Leon F A G
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 Krüger
AU - Webb, Penelope M
AU - Ness, Roberta B
AU - Menon, Usha
AU - Berchuck, Andrew
AU - Pharoah, Paul D
AU - Risch, Harvey A
AU - Leigh Pearce, Celeste
AU - Mukherjee, Bhramar
N1 - © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PY - 2017/6/14
Y1 - 2017/6/14
N2 - 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 the 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. This paper extends the empirical Bayes (EB) approach previously developed for multiplicative interaction that trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of Relative Excess Risk due to Interaction is derived and the corresponding Wald test is proposed with 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 power gain compared to the standard logistic regression analysis and better control of Type I error when compared to the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
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 the 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. This paper extends the empirical Bayes (EB) approach previously developed for multiplicative interaction that trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of Relative Excess Risk due to Interaction is derived and the corresponding Wald test is proposed with 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 power gain compared to the standard logistic regression analysis and better control of Type I error when compared to the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
KW - Journal Article
U2 - 10.1093/aje/kwx243
DO - 10.1093/aje/kwx243
M3 - SCORING: Journal article
C2 - 28633381
JO - AM J EPIDEMIOL
JF - AM J EPIDEMIOL
SN - 0002-9262
ER -