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@ARTICLE{Zucknick:127886,
      author       = {M. Zucknick$^*$ and M. Saadati$^*$ and A. Benner$^*$},
      title        = {{N}onidentical twins: {C}omparison of frequentist and
                      {B}ayesian lasso for {C}ox models.},
      journal      = {Biometrical journal},
      volume       = {57},
      number       = {6},
      issn         = {0323-3847},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2017-03908},
      pages        = {959 - 981},
      year         = {2015},
      abstract     = {One important task in translational cancer research is the
                      search for new prognostic biomarkers to improve survival
                      prognosis for patients. The use of high-throughput
                      technologies allows simultaneous measurement of genome-wide
                      gene expression or other genomic data for all patients in a
                      clinical trial. Penalized likelihood methods such as lasso
                      regression can be applied to such high-dimensional data,
                      where the number of (genomic) covariables is usually much
                      larger than the sample size. There is a connection between
                      the lasso and the Bayesian regression model with independent
                      Laplace priors on the regression parameters, and
                      understanding this connection has been useful for
                      understanding the properties of lasso estimates in linear
                      models (e.g. Park and Casella, 2008). In this paper, we
                      study the lasso in the frequentist and Bayesian frameworks
                      in the context of Cox models. For the Bayesian lasso we
                      extend the approach by Lee et al. (2011). In particular, we
                      impose the lasso penalty only on the genome features, but
                      not on relevant clinical covariates, to allow the mandatory
                      inclusion of important established factors. We investigate
                      the models in high- and low-dimensional simulation settings
                      and in an application to chronic lymphocytic leukemia.},
      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:26417963},
      doi          = {10.1002/bimj.201400160},
      url          = {https://inrepo02.dkfz.de/record/127886},
}