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@ARTICLE{DeBin:147704,
      author       = {R. De Bin and A.-L. Boulesteix and A. Benner$^*$ and N.
                      Becker$^*$ and W. Sauerbrei},
      title        = {{C}ombining clinical and molecular data in regression
                      prediction models: insights from a simulation study.},
      journal      = {Briefings in bioinformatics},
      volume       = {21},
      number       = {6},
      issn         = {1477-4054},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2019-02681},
      pages        = {1904-1919},
      year         = {2020},
      note         = {2020 Dec 1;21(6):1904-1919},
      abstract     = {Data integration, i.e. the use of different sources of
                      information for data analysis, is becoming one of the most
                      important topics in modern statistics. Especially in, but
                      not limited to, biomedical applications, a relevant issue is
                      the combination of low-dimensional (e.g. clinical data) and
                      high-dimensional (e.g. molecular data such as gene
                      expressions) data sources in a prediction model. Not only
                      the different characteristics of the data, but also the
                      complex correlation structure within and between the two
                      data sources, pose challenging issues. In this paper, we
                      investigate these issues via simulations, providing some
                      useful insight into strategies to combine low- and
                      high-dimensional data in a regression prediction model. In
                      particular, we focus on the effect of the correlation
                      structure on the results, while accounting for the influence
                      of our specific choices in the design of the simulation
                      study.},
      subtyp        = {Review Article},
      cin          = {C060},
      ddc          = {004},
      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:31750518},
      doi          = {10.1093/bib/bbz136},
      url          = {https://inrepo02.dkfz.de/record/147704},
}