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@ARTICLE{Rahnenfhrer:276080,
      author       = {J. Rahnenführer and R. De Bin and A. Benner$^*$ and F.
                      Ambrogi and L. Lusa and A.-L. Boulesteix and E. Migliavacca
                      and H. Binder and S. Michiels and W. Sauerbrei and L.
                      McShane},
      collaboration = {f. t. g. “. data”},
      title        = {{S}tatistical analysis of high-dimensional biomedical data:
                      a gentle introduction to analytical goals, common approaches
                      and challenges.},
      journal      = {BMC medicine},
      volume       = {21},
      number       = {1},
      issn         = {1741-7015},
      address      = {Heidelberg [u.a.]},
      publisher    = {Springer},
      reportid     = {DKFZ-2023-00986},
      pages        = {182},
      year         = {2023},
      abstract     = {In high-dimensional data (HDD) settings, the number of
                      variables associated with each observation is very large.
                      Prominent examples of HDD in biomedical research include
                      omics data with a large number of variables such as many
                      measurements across the genome, proteome, or metabolome, as
                      well as electronic health records data that have large
                      numbers of variables recorded for each patient. The
                      statistical analysis of such data requires knowledge and
                      experience, sometimes of complex methods adapted to the
                      respective research questions.Advances in statistical
                      methodology and machine learning methods offer new
                      opportunities for innovative analyses of HDD, but at the
                      same time require a deeper understanding of some fundamental
                      statistical concepts. Topic group TG9 'High-dimensional
                      data' of the STRATOS (STRengthening Analytical Thinking for
                      Observational Studies) initiative provides guidance for the
                      analysis of observational studies, addressing particular
                      statistical challenges and opportunities for the analysis of
                      studies involving HDD. In this overview, we discuss key
                      aspects of HDD analysis to provide a gentle introduction for
                      non-statisticians and for classically trained statisticians
                      with little experience specific to HDD.The paper is
                      organized with respect to subtopics that are most relevant
                      for the analysis of HDD, in particular initial data
                      analysis, exploratory data analysis, multiple testing, and
                      prediction. For each subtopic, main analytical goals in HDD
                      settings are outlined. For each of these goals, basic
                      explanations for some commonly used analysis methods are
                      provided. Situations are identified where traditional
                      statistical methods cannot, or should not, be used in the
                      HDD setting, or where adequate analytic tools are still
                      lacking. Many key references are provided.This review aims
                      to provide a solid statistical foundation for researchers,
                      including statisticians and non-statisticians, who are new
                      to research with HDD or simply want to better evaluate and
                      understand the results of HDD analyses.},
      subtyp        = {Review Article},
      keywords     = {Humans / Goals / Biomedical Research / Research Design /
                      Analytical goals (Other) / Clustering (Other) / Exploratory
                      data analysis (Other) / High-dimensional data (Other) /
                      Initial data analysis (Other) / Multiple testing (Other) /
                      Omics data (Other) / Prediction (Other) / STRATOS initiative
                      (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:37189125},
      pmc          = {pmc:PMC10186672},
      doi          = {10.1186/s12916-023-02858-y},
      url          = {https://inrepo02.dkfz.de/record/276080},
}