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

for the Ovarian Cancer Association Consortium

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)366-377
Number of pages12
JournalAmerican journal of epidemiology
Volume187
Issue number2
DOIs
StatePublished - Feb 1 2018

Keywords

  • bias-variance tradeoff
  • effect modification
  • empirical Bayes estimation
  • genetic risk score
  • relative excess risk
  • shrinkage

ASJC Scopus subject areas

  • Epidemiology

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