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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.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung

Harvard

Liu, G, Lee, S, Lee, AW, Wu, AH, Bandera, EV, Jensen, A, Anne Rossing, M, Moysich, KB, Chang-Claude, J, Doherty, JA, Gentry-Maharaj, A, Kiemeney, LA, Gayther, SA, Modugno, F, Massuger, LFAG, Goode, EL, Fridley, BL, Terry, KL, Cramer, DW, Ramus, SJ, Anton-Culver, H, Ziogas, A, Tyrer, JP, Schildkraut, JM, Kjaer, SK, Webb, PM, Ness, RB, Menon, U, Berchuck, A, Pharoah, PD, Risch, HA, Leigh Pearce, C & Mukherjee, B 2017, 'Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence', AM J EPIDEMIOL. https://doi.org/10.1093/aje/kwx243

APA

Liu, G., Lee, S., Lee, A. W., Wu, A. H., Bandera, E. V., Jensen, A., Anne Rossing, M., Moysich, K. B., Chang-Claude, J., Doherty, J. A., Gentry-Maharaj, A., Kiemeney, L. A., Gayther, S. A., Modugno, F., Massuger, L. F. A. G., Goode, E. L., Fridley, B. L., Terry, K. L., Cramer, D. W., ... Mukherjee, B. (2017). Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. AM J EPIDEMIOL. https://doi.org/10.1093/aje/kwx243

Vancouver

Liu G, Lee S, Lee AW, Wu AH, Bandera EV, Jensen A et al. Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. AM J EPIDEMIOL. 2017 Jun 14. https://doi.org/10.1093/aje/kwx243

Bibtex

@article{6813d09301664e36acdafb76e391e393,
title = "Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence",
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 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.",
keywords = "Journal Article",
author = "Gang Liu and Seunggeun Lee and Lee, {Alice W} and Wu, {Anna H} and Bandera, {Elisa V} and Allan Jensen and {Anne Rossing}, Mary and Moysich, {Kirsten B} and Jenny Chang-Claude and Doherty, {Jennifer Anne} and Aleksandra Gentry-Maharaj and Kiemeney, {Lambertus A} and Gayther, {Simon A} and Francesmary Modugno and Massuger, {Leon F A G} and Goode, {Ellen L} and Fridley, {Brooke L} and Terry, {Kathryn L} and Cramer, {Daniel W} and Ramus, {Susan J} and Hoda Anton-Culver and Argyrios Ziogas and Tyrer, {Jonathan P} and Schildkraut, {Joellen M} and Kjaer, {Susanne Kr{\"u}ger} and Webb, {Penelope M} and Ness, {Roberta B} and Usha Menon and Andrew Berchuck and Pharoah, {Paul D} and Risch, {Harvey A} and {Leigh Pearce}, Celeste and Bhramar Mukherjee",
note = "{\textcopyright} 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.",
year = "2017",
month = jun,
day = "14",
doi = "10.1093/aje/kwx243",
language = "English",
journal = "AM J EPIDEMIOL",
issn = "0002-9262",
publisher = "Oxford University Press",

}

RIS

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 -