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@ARTICLE{HollandLetz:124361,
      author       = {T. Holland-Letz$^*$},
      title        = {{O}n the combination of c- and {D}-optimal designs:
                      {G}eneral approaches and applications in dose-response
                      studies.},
      journal      = {Biometrics},
      volume       = {73},
      number       = {1},
      issn         = {0006-341X},
      address      = {Washington, DC},
      publisher    = {Internat. Biometric Soc.},
      reportid     = {DKFZ-2017-01240},
      pages        = {206 - 213},
      year         = {2017},
      abstract     = {Dose-response modeling in areas such as toxicology is often
                      conducted using a parametric approach. While estimation of
                      parameters is usually one of the goals, often the main aim
                      of the study is the estimation of quantities derived from
                      the parameters, such as the ED50 dose. From the view of
                      statistical optimal design theory such an objective
                      corresponds to a c-optimal design criterion. Unfortunately,
                      c-optimal designs often create practical problems, and
                      furthermore commonly do not allow actual estimation of the
                      parameters. It is therefore useful to consider alternative
                      designs which show good c-performance, while still being
                      applicable in practice and allowing reasonably good general
                      parameter estimation. In effect, using optimal design
                      terminology this means that a reasonable performance
                      regarding the D-criterion is expected as well. In this
                      article, we propose several approaches to the task of
                      combining c- and D-efficient designs, such as using mixed
                      information functions or setting minimum requirements
                      regarding either c- or D-efficiency, and show how to
                      algorithmically determine optimal designs in each case. We
                      apply all approaches to a standard situation from
                      toxicology, and obtain a much better balance between c- and
                      D-performance. Next, we investigate how to adapt the designs
                      to different parameter values. Finally, we show that the
                      methodology used here is not just limited to the combination
                      of c- and D-designs, but can also be used to handle more
                      general constraint situations such as limits on the cost of
                      an experiment.},
      cin          = {C060},
      ddc          = {570},
      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:27218478},
      doi          = {10.1111/biom.12545},
      url          = {https://inrepo02.dkfz.de/record/124361},
}