% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Krzykalla:148748,
      author       = {J. Krzykalla$^*$ and A. Benner$^*$ and A.
                      Kopp-Schneider$^*$},
      title        = {{E}xploratory identification of predictive biomarkers in
                      randomized trials with normal endpoints.},
      journal      = {Statistics in medicine},
      volume       = {39},
      number       = {7},
      issn         = {0277-6715},
      address      = {Chichester [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2019-03261},
      pages        = {923-939},
      year         = {2020},
      note         = {#EA:C060#LA:C060#2020 Mar 30;39(7):923-939},
      abstract     = {One of the main endeavours in present-day medicine,
                      especially in oncological research, is to provide evidence
                      for individual treatment decisions ('stratified medicine').
                      In the pursuit of optimal treatment decision rules, the
                      identification of predictive biomarkers that modify the
                      treatment effect is essential. Proposed methods have often
                      been based on recursive partitioning since a wide variety of
                      interaction patterns can be captured automatically and the
                      results are easily interpretable. Furthermore, these methods
                      are readily extendable to high-dimensional settings by means
                      of ensemble learning. In this article, we present predMOB,
                      an adaptation of the model-based recursive partitioning
                      (MOB) for subgroup analysis approach specifically tailored
                      to the identification of predictive factors. In a simulation
                      study, predMOB outperforms the original MOB with respect to
                      the number of false detections and shows to be more robust
                      in moderately complex settings. Furthermore, we compare the
                      results of predMOB for the application to a public data base
                      of amyotrophic lateral sclerosis patients to those obtained
                      from the original MOB and are able to elucidate the nature
                      of the biomarkers' effects.},
      cin          = {C060},
      ddc          = {610},
      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:31863499},
      doi          = {10.1002/sim.8452},
      url          = {https://inrepo02.dkfz.de/record/148748},
}