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@ARTICLE{Beisel:120669,
      author       = {C. Beisel$^*$ and A. Benner$^*$ and C. Kunz$^*$ and A.
                      Kopp-Schneider$^*$},
      title        = {{H}eterogeneous treatment effects in stratified clinical
                      trials with time-to-event endpoints.},
      journal      = {Biometrical journal},
      volume       = {59},
      number       = {3},
      issn         = {0323-3847},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2017-01095},
      pages        = {511 - 530},
      year         = {2017},
      abstract     = {When analyzing clinical trials with a stratified
                      population, homogeneity of treatment effects is a common
                      assumption in survival analysis. However, in the context of
                      recent developments in clinical trial design, which aim to
                      test multiple targeted therapies in corresponding
                      subpopulations simultaneously, the assumption that there is
                      no treatment-by-stratum interaction seems inappropriate. It
                      becomes an issue if the expected sample size of the strata
                      makes it unfeasible to analyze the trial arms individually.
                      Alternatively, one might choose as primary aim to prove
                      efficacy of the overall (targeted) treatment strategy. When
                      testing for the overall treatment effect, a violation of the
                      no-interaction assumption renders it necessary to deviate
                      from standard methods that rely on this assumption. We
                      investigate the performance of different methods for sample
                      size calculation and data analysis under heterogeneous
                      treatment effects. The commonly used sample size formula by
                      Schoenfeld is compared to another formula by Lachin and
                      Foulkes, and to an extension of Schoenfeld's formula
                      allowing for stratification. Beyond the widely used
                      (stratified) Cox model, we explore the lognormal shared
                      frailty model, and a two-step analysis approach as potential
                      alternatives that attempt to adjust for interstrata
                      heterogeneity. We carry out a simulation study for a trial
                      with three strata and violations of the no-interaction
                      assumption. The extension of Schoenfeld's formula to
                      heterogeneous strata effects provides the most reliable
                      sample size with respect to desired versus actual power. The
                      two-step analysis and frailty model prove to be more robust
                      against loss of power caused by heterogeneous treatment
                      effects than the stratified Cox model and should be
                      preferred in such situations.},
      cin          = {C060},
      ddc          = {570},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:28263395},
      doi          = {10.1002/bimj.201600047},
      url          = {https://inrepo02.dkfz.de/record/120669},
}