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@ARTICLE{Liu:132495,
      author       = {G. Liu and B. Mukherjee and S. Lee and A. W. Lee and A. H.
                      Wu and E. V. Bandera and A. Jensen and M. A. Rossing and K.
                      B. Moysich and J. Chang-Claude$^*$ and J. A. Doherty and A.
                      Gentry-Maharaj and L. Kiemeney and S. A. Gayther and F.
                      Modugno and L. Massuger and E. L. Goode and B. L. Fridley
                      and K. L. Terry and D. W. Cramer and S. J. Ramus and H.
                      Anton-Culver and A. Ziogas and J. P. Tyrer and J. M.
                      Schildkraut and S. K. Kjaer and P. M. Webb and R. B. Ness
                      and U. Menon and A. Berchuck and P. D. Pharoah and H. Risch
                      and C. L. Pearce},
      collaboration = {O. C. A. Consortium},
      title        = {{R}obust {T}ests for {A}dditive {G}ene-{E}nvironment
                      {I}nteraction in {C}ase-{C}ontrol {S}tudies {U}sing
                      {G}ene-{E}nvironment {I}ndependence.},
      journal      = {American journal of epidemiology},
      volume       = {187},
      number       = {2},
      issn         = {1476-6256},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2018-00182},
      pages        = {366 - 377},
      year         = {2018},
      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.},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28633381},
      doi          = {10.1093/aje/kwx243},
      url          = {https://inrepo02.dkfz.de/record/132495},
}