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@ARTICLE{Calderazzo:157332,
      author       = {S. Calderazzo$^*$ and M. Wiesenfarth$^*$ and A.
                      Kopp-Schneider$^*$},
      title        = {{A} decision-theoretic approach to {B}ayesian clinical
                      trial design and evaluation of robustness to prior-data
                      conflict.},
      journal      = {Biostatistics},
      volume       = {23},
      number       = {1},
      issn         = {1468-4357},
      address      = {Oxford [u.a.]},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2020-01561},
      pages        = {328–344},
      year         = {2022},
      note         = {Volume 23, Issue 1, January 2022, Pages 328–344},
      abstract     = {Bayesian clinical trials allow taking advantage of relevant
                      external information through the elicitation of prior
                      distributions, which influence Bayesian posterior parameter
                      estimates and test decisions. However, incorporation of
                      historical information can have harmful consequences on the
                      trial's frequentist (conditional) operating characteristics
                      in case of inconsistency between prior information and the
                      newly collected data. A compromise between meaningful
                      incorporation of historical information and strict control
                      of frequentist error rates is therefore often sought. Our
                      aim is thus to review and investigate the rationale and
                      consequences of different approaches to relaxing strict
                      frequentist control of error rates from a Bayesian
                      decision-theoretic viewpoint. In particular, we define an
                      integrated risk which incorporates losses arising from
                      testing, estimation, and sampling. A weighted combination of
                      the integrated risk addends arising from testing and
                      estimation allows moving smoothly between these two targets.
                      Furthermore, we explore different possible elicitations of
                      the test error costs, leading to test decisions based either
                      on posterior probabilities, or solely on Bayes factors.
                      Sensitivity analyses are performed following the convention
                      which makes a distinction between the prior of the
                      data-generating process, and the analysis prior adopted to
                      fit the data. Simulation in the case of normal and binomial
                      outcomes and an application to a one-arm proof-of-concept
                      trial, exemplify how such analysis can be conducted to
                      explore sensitivity of the integrated risk, the operating
                      characteristics, and the optimal sample size, to prior-data
                      conflict. Robust analysis prior specifications, which
                      gradually discount potentially conflicting prior
                      information, are also included for comparison. Guidance with
                      respect to cost elicitation, particularly in the context of
                      a Phase II proof-of-concept trial, is provided.},
      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:32735010},
      doi          = {10.1093/biostatistics/kxaa027},
      url          = {https://inrepo02.dkfz.de/record/157332},
}