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Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

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Liu, G, Lee, S, Lee, AW, Wu, AH, Bandera, EV, Jensen, A, Anne Rossing, M, Moysich, KB, Chang-Claude, J, Doherty, J, Gentry-Maharaj, A, Kiemeney, L, Gayther, SA, Modugno, F, Massuger, L, Goode, EL, Fridley, B, 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, H, Leigh Pearce, C & Mukherjee, B 2018, 'Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence' American Journal of Epidemiology, bind 187, nr. 2, s. 366-77. https://doi.org/10.1093/aje/kwx243

APA

CBE

Liu G, Lee S, Lee AW, Wu AH, Bandera EV, Jensen A, Anne Rossing M, Moysich KB, Chang-Claude J, Doherty J, Gentry-Maharaj A, Kiemeney L, Gayther SA, Modugno F, Massuger L, Goode EL, Fridley B, 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 H, Leigh Pearce C, Mukherjee B. 2018. Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. American Journal of Epidemiology. 187(2):366-77. https://doi.org/10.1093/aje/kwx243

MLA

Vancouver

Author

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 ; Gentry-Maharaj, Aleksandra ; Kiemeney, Lambertus ; Gayther, Simon A ; Modugno, Francesmary ; Massuger, Leon ; Goode, Ellen L ; Fridley, Brooke ; Terry, Kathryn L ; Cramer, Daniel W ; Ramus, Susan J ; Anton-Culver, Hoda ; Ziogas, Argyrios ; Tyrer, Jonathan P ; Schildkraut, Joellen M ; Kjaer, Susanne K ; Webb, Penelope M ; Ness, Roberta B ; Menon, Usha ; Berchuck, Andrew ; Pharoah, Paul D ; Risch, Harvey ; Leigh Pearce, Celeste ; Mukherjee, Bhramar. / Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence. I: American Journal of Epidemiology. 2018 ; Bind 187, Nr. 2. s. 366-77.

Bibtex

@article{964850a79b394436963a83c74e4a56cc,
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 Jennifer Doherty and Aleksandra Gentry-Maharaj and Lambertus Kiemeney and Gayther, {Simon A} and Francesmary Modugno and Leon Massuger and Goode, {Ellen L} and Brooke Fridley 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 K} and Webb, {Penelope M} and Ness, {Roberta B} and Usha Menon and Andrew Berchuck and Pharoah, {Paul D} and Harvey Risch and {Leigh Pearce}, Celeste and Bhramar Mukherjee",
note = "{\circledC} 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 = "2018",
doi = "10.1093/aje/kwx243",
language = "English",
volume = "187",
pages = "366--77",
journal = "American Journal of Epidemiology",
issn = "0002-9262",
publisher = "Oxford University Press",
number = "2",

}

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

AU - Gentry-Maharaj, Aleksandra

AU - Kiemeney, Lambertus

AU - Gayther, Simon A

AU - Modugno, Francesmary

AU - Massuger, Leon

AU - Goode, Ellen L

AU - Fridley, Brooke

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 - 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 - 2018

Y1 - 2018

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 - Journal article

VL - 187

SP - 366

EP - 377

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

SN - 0002-9262

IS - 2

ER -

ID: 52206758