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@ARTICLE{Saadati:132668,
      author       = {M. Saadati$^*$ and J. Beyersmann and A. Kopp-Schneider$^*$
                      and A. Benner$^*$},
      title        = {{P}rediction accuracy and variable selection for penalized
                      cause-specific hazards models.},
      journal      = {Biometrical journal},
      volume       = {60},
      number       = {2},
      issn         = {0323-3847},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2018-00328},
      pages        = {288 - 306},
      year         = {2018},
      abstract     = {We consider modeling competing risks data in high
                      dimensions using a penalized cause-specific hazards (CSHs)
                      approach. CSHs have conceptual advantages that are useful
                      for analyzing molecular data. First, working on hazards
                      level can further understanding of the underlying biological
                      mechanisms that drive transition hazards. Second, CSH models
                      can be used to extend the multistate framework for
                      high-dimensional data. The CSH approach is implemented by
                      fitting separate proportional hazards models for each event
                      type (iCS). In the high-dimensional setting, this might seem
                      too complex and possibly prone to overfitting. Therefore, we
                      consider an extension, namely 'linking' the separate models
                      by choosing penalty tuning parameters that in combination
                      yield best prediction of the incidence of the event of
                      interest (penCR). We investigate whether this extension is
                      useful with respect to prediction accuracy and variable
                      selection. The two approaches are compared to the
                      subdistribution hazards (SDH) model, which is an established
                      method that naturally achieves 'linking' by working on
                      incidence level, but loses interpretability of the covariate
                      effects. Our simulation studies indicate that in many
                      aspects, iCS is competitive to penCR and the SDH approach.
                      There are some instances that speak in favor of linking the
                      CSH models, for example, in the presence of opposing effects
                      on the CSHs. We conclude that penalized CSH models are a
                      viable solution for competing risks models in high
                      dimensions. Linking the CSHs can be useful in some
                      particular cases; however, simple models using separately
                      penalized CSH are often justified.},
      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:28762523},
      doi          = {10.1002/bimj.201600242},
      url          = {https://inrepo02.dkfz.de/record/132668},
}