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@ARTICLE{Edelmann:147214,
      author       = {D. Edelmann$^*$ and C. Habermehl and R. F. Schlenk and A.
                      Benner$^*$},
      title        = {{A}djusting {S}imon's optimal two-stage design for
                      heterogeneous populations based on stratification or using
                      historical controls.},
      journal      = {Biometrical journal},
      volume       = {62},
      number       = {2},
      issn         = {1521-4036},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2019-02340},
      pages        = {311-329},
      year         = {2020},
      note         = {2020 Mar;62(2):311-329#EA:C060#LA:C060#},
      abstract     = {In many cancer studies, the population under consideration
                      is highly heterogeneous in terms of clinical, demographical,
                      and biological covariates. As the covariates substantially
                      impact the individual prognosis, the response probabilities
                      of patients entering the study may strongly vary. In this
                      case, the operating characteristics of classical clinical
                      trial designs heavily depend on the covariates of patients
                      entering the study. Notably, both type I and type II errors
                      can be much higher than specified. In this paper, two
                      modifications of Simon's optimal two-stage design correcting
                      for heterogeneous populations are derived. The first
                      modification assumes that the patient population is divided
                      into a finite number of subgroups, where each subgroup has a
                      different response probability. The second approach uses a
                      logistic regression model based on historical controls to
                      estimate the response probabilities of patients entering the
                      study. The performance of both approaches is demonstrated
                      using simulation examples.},
      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:31553076},
      doi          = {10.1002/bimj.201800390},
      url          = {https://inrepo02.dkfz.de/record/147214},
}