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@ARTICLE{Edelmann:157542,
      author       = {D. Edelmann$^*$ and K. Ohneberg and N. Becker and A.
                      Benner$^*$ and M. Schumacher},
      title        = {{W}hich patients to sample in clinical cohort studies when
                      the number of events is high and measurement of additional
                      markers is constrained by limited resources.},
      journal      = {Cancer medicine},
      volume       = {9},
      number       = {20},
      issn         = {2045-7634},
      address      = {Hoboken, NJ},
      publisher    = {Wiley},
      reportid     = {DKFZ-2020-01697},
      pages        = {7398-7406},
      year         = {2020},
      note         = {#EA:C060#LA:C060# / 2020 Oct;9(20):7398-7406},
      abstract     = {We consider an existing clinical cohort with events but
                      limited resources for the investigation of a further
                      potentially expensive marker. Biological material of the
                      patients is stored in a biobank, but only a limited number
                      of samples can be analyzed with respect to the marker. The
                      question arises as to which patients to sample, if the
                      number of events preclude standard sampling
                      designs.Modifications of the nested case-control and the
                      case-cohort design for the proportional hazards model are
                      applied, that allow efficient sampling in situations where
                      standard nested case-control and case-cohort are not
                      feasible. These sampling designs are compared to simple
                      random sampling and extreme group sampling, the latter
                      including only patients with extreme outcomes, ie either
                      with an event early in time or without an event until at
                      least a point later in time.The modified nested case-control
                      design and the modified case-cohort design provide powerful
                      methods for sampling in a clinical cohort with many events.
                      The simple random sampling usually is less efficient. If
                      focus is on precise estimation of a potential effect in
                      terms of a hazard ratio, extreme group sampling is not
                      competitive. If focus is on screening for important
                      biomarkers, extreme group sampling markedly outperforms the
                      other sampling designs.When it is not feasible to sample all
                      events, a modified nested case-control design or case-cohort
                      design leads to efficient effect estimates in the
                      proportional hazards model. If screening for important
                      biomarkers is the primary objective, extreme group sampling
                      is preferable.},
      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:32813923},
      doi          = {10.1002/cam4.3381},
      url          = {https://inrepo02.dkfz.de/record/157542},
}