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@ARTICLE{Schreck:288132,
      author       = {N. Schreck$^*$ and A. Slynko and M. Saadati$^*$ and A.
                      Benner$^*$},
      title        = {{S}tatistical plasmode simulations-{P}otentials, challenges
                      and recommendations.},
      journal      = {Statistics in medicine},
      volume       = {43},
      number       = {9},
      issn         = {0277-6715},
      address      = {Chichester [u.a.]},
      publisher    = {Wiley},
      reportid     = {DKFZ-2024-00353},
      pages        = {1804-1825},
      year         = {2024},
      note         = {TUTORIAL IN BIOSTATISTICS / #EA:C060#LA:C060# / 2024 Apr
                      30;43(9):1804-1825},
      abstract     = {Statistical data simulation is essential in the development
                      of statistical models and methods as well as in their
                      performance evaluation. To capture complex data structures,
                      in particular for high-dimensional data, a variety of
                      simulation approaches have been introduced including
                      parametric and the so-called plasmode simulations. While
                      there are concerns about the realism of parametrically
                      simulated data, it is widely claimed that plasmodes come
                      very close to reality with some aspects of the 'truth'
                      known. However, there are no explicit guidelines or
                      state-of-the-art on how to perform plasmode data
                      simulations. In the present paper, we first review existing
                      literature and introduce the concept of statistical plasmode
                      simulation. We then discuss advantages and challenges of
                      statistical plasmodes and provide a step-wise procedure for
                      their generation, including key steps to their
                      implementation and reporting. Finally, we illustrate the
                      concept of statistical plasmodes as well as the proposed
                      plasmode generation procedure by means of a public real RNA
                      data set on breast carcinoma patients.},
      keywords     = {data-generating process (Other) / outcome-generating model
                      (Other) / parametric simulations (Other) / resampling
                      (Other) / statistical plasmodes (Other)},
      cin          = {C060},
      ddc          = {610},
      cid          = {I:(DE-He78)C060-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38356231},
      doi          = {10.1002/sim.10012},
      url          = {https://inrepo02.dkfz.de/record/288132},
}