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Region Hovedstaden - en del af Københavns Universitetshospital
Udgivet

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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  • Gang Liu
  • Seunggeun Lee
  • Alice W Lee
  • Anna H Wu
  • Elisa V Bandera
  • Allan Jensen
  • Mary Anne Rossing
  • Kirsten B Moysich
  • Jenny Chang-Claude
  • Jennifer Doherty
  • Aleksandra Gentry-Maharaj
  • Lambertus Kiemeney
  • Simon A Gayther
  • Francesmary Modugno
  • Leon Massuger
  • Ellen L Goode
  • Brooke Fridley
  • Kathryn L Terry
  • Daniel W Cramer
  • Susan J Ramus
  • Hoda Anton-Culver
  • Argyrios Ziogas
  • Jonathan P Tyrer
  • Joellen M Schildkraut
  • Susanne K Kjaer
  • Penelope M Webb
  • Roberta B Ness
  • Usha Menon
  • Andrew Berchuck
  • Paul D Pharoah
  • Harvey Risch
  • Celeste Leigh Pearce
  • Bhramar Mukherjee
Vis graf over relationer

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.

OriginalsprogEngelsk
TidsskriftAmerican Journal of Epidemiology
Vol/bind187
Udgave nummer2
Sider (fra-til)366-77
ISSN0002-9262
DOI
StatusUdgivet - 2018

ID: 52206758