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@ARTICLE{Habermehl:132643,
      author       = {C. Habermehl$^*$ and A. Benner$^*$ and A.
                      Kopp-Schneider$^*$},
      title        = {{A}ddressing small sample size bias in multiple-biomarker
                      trials: {I}nclusion of biomarker-negative patients and
                      {F}irth correction.},
      journal      = {Biometrical journal},
      volume       = {60},
      number       = {2},
      issn         = {0323-3847},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2018-00303},
      pages        = {275 - 287},
      year         = {2018},
      abstract     = {In recent years, numerous approaches for biomarker-based
                      clinical trials have been developed. One of these
                      developments are multiple-biomarker trials, which aim to
                      investigate multiple biomarkers simultaneously in
                      independent subtrials. For low-prevalence biomarkers, small
                      sample sizes within the subtrials have to be expected, as
                      well as many biomarker-negative patients at the screening
                      stage. The small sample sizes may make it unfeasible to
                      analyze the subtrials individually. This imposes the need to
                      develop new approaches for the analysis of such trials. With
                      an expected large group of biomarker-negative patients, it
                      seems reasonable to explore options to benefit from
                      including them in such trials. We consider advantages and
                      disadvantages of the inclusion of biomarker-negative
                      patients in a multiple-biomarker trial with a survival
                      endpoint. We discuss design options that include
                      biomarker-negative patients in the study and address the
                      issue of small sample size bias in such trials. We carry out
                      a simulation study for a design where biomarker-negative
                      patients are kept in the study and are treated with standard
                      of care. We compare three different analysis approaches
                      based on the Cox model to examine if the inclusion of
                      biomarker-negative patients can provide a benefit with
                      respect to bias and variance of the treatment effect
                      estimates. We apply the Firth correction to reduce the small
                      sample size bias. The results of the simulation study
                      suggest that for small sample situations, the Firth
                      correction should be applied to adjust for the small sample
                      size bias. Additional to the Firth penalty, the inclusion of
                      biomarker-negative patients in the analysis can lead to
                      further but small improvements in bias and standard
                      deviation of the estimates.},
      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:28762532},
      doi          = {10.1002/bimj.201600226},
      url          = {https://inrepo02.dkfz.de/record/132643},
}