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@ARTICLE{HollandLetz:126744,
      author       = {T. Holland-Letz$^*$ and A. Kopp-Schneider$^*$},
      title        = {{O}ptimal experimental designs for dose-response studies
                      with continuous endpoints.},
      journal      = {Archives of toxicology},
      volume       = {89},
      number       = {11},
      issn         = {1432-0738},
      address      = {Berlin},
      publisher    = {Springer},
      reportid     = {DKFZ-2017-02772},
      pages        = {2059 - 2068},
      year         = {2015},
      abstract     = {In most areas of clinical and preclinical research, the
                      required sample size determines the costs and effort for any
                      project, and thus, optimizing sample size is of primary
                      importance. An experimental design of dose-response studies
                      is determined by the number and choice of dose levels as
                      well as the allocation of sample size to each level. The
                      experimental design of toxicological studies tends to be
                      motivated by convention. Statistical optimal design theory,
                      however, allows the setting of experimental conditions (dose
                      levels, measurement times, etc.) in a way which minimizes
                      the number of required measurements and subjects to obtain
                      the desired precision of the results. While the general
                      theory is well established, the mathematical complexity of
                      the problem so far prevents widespread use of these
                      techniques in practical studies. The paper explains the
                      concepts of statistical optimal design theory with a minimum
                      of mathematical terminology and uses these concepts to
                      generate concrete usable D-optimal experimental designs for
                      dose-response studies on the basis of three common
                      dose-response functions in toxicology: log-logistic,
                      log-normal and Weibull functions with four parameters each.
                      The resulting designs usually require control plus only
                      three dose levels and are quite intuitively plausible. The
                      optimal designs are compared to traditional designs such as
                      the typical setup of cytotoxicity studies for 96-well
                      plates. As the optimal design depends on prior estimates of
                      the dose-response function parameters, it is shown what loss
                      of efficiency occurs if the parameters for design
                      determination are misspecified, and how Bayes optimal
                      designs can improve the situation.},
      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:25155192},
      pmc          = {pmc:PMC4655015},
      doi          = {10.1007/s00204-014-1335-2},
      url          = {https://inrepo02.dkfz.de/record/126744},
}