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@ARTICLE{HollandLetz:164060,
      author       = {T. Holland-Letz$^*$ and A. Kopp-Schneider$^*$},
      title        = {{T}he design heatmap: {A} simple visualization of {D}
                      -optimality design problems.},
      journal      = {Biometrical journal},
      volume       = {62},
      number       = {8},
      issn         = {1521-4036},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2020-02228},
      pages        = {2013-2031},
      year         = {2020},
      note         = {#EA:C060#LA:C060# / Volume62, Issue 8 December 2020 Pages
                      2013-2031},
      abstract     = {Optimal experimental designs are often formal and specific,
                      and not intuitively plausible to practical experimenters.
                      However, even in theory, there often are many different
                      possible design points providing identical or nearly
                      identical information compared to the design points of a
                      strictly optimal design. In practical applications, this can
                      be used to find designs that are a compromise between
                      mathematical optimality and practical requirements,
                      including preferences of experimenters. For this purpose, we
                      propose a derivative-based two-dimensional graphical
                      representation of the design space that, given any optimal
                      design is already known, will show which areas of the design
                      space are relevant for good designs and how these areas
                      relate to each other. While existing equivalence theorems
                      already allow such an illustration in regard to the
                      relevance of design points only, our approach also shows
                      whether different design points contribute the same kind of
                      information, and thus allows tweaking of designs for
                      practical applications, especially in regard to the
                      splitting and combining of design points. We demonstrate the
                      approach on a toxicological trial where a D -optimal design
                      for a dose-response experiment modeled by a four-parameter
                      log-logistic function was requested. As these designs
                      require a prior estimate of the relevant parameters, which
                      is difficult to obtain in a practical situation, we also
                      discuss an adaption of our representations to the criterion
                      of Bayesian D -optimality. While we focus on D -optimality,
                      the approach is in principle applicable to different
                      optimality criteria as well. However, much of the
                      computational and graphical simplicity will be lost.},
      cin          = {C060},
      ddc          = {570},
      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:33058202},
      doi          = {10.1002/bimj.202000087},
      url          = {https://inrepo02.dkfz.de/record/164060},
}