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@ARTICLE{Erdmann:277785,
      author       = {S. Erdmann and D. Edelmann$^*$ and M. Kieser},
      title        = {{U}sing real-world data to predict health outcomes-{T}he
                      prediction design: {A}pplication and sample size planning.},
      journal      = {Biometrical journal},
      volume       = {65},
      number       = {6},
      issn         = {0006-3452},
      address      = {Berlin},
      publisher    = {Wiley-VCH},
      reportid     = {DKFZ-2023-01499},
      pages        = {e2200023},
      year         = {2023},
      note         = {2023 Aug;65(6):e2200023},
      abstract     = {The gold standard for investigating the efficacy of a new
                      therapy is a (pragmatic) randomized controlled trial (RCT).
                      This approach is costly, time-consuming, and not always
                      practicable. At the same time, huge quantities of available
                      patient-level control condition data in analyzable format of
                      (former) RCTs or real-world data (RWD) are neglected.
                      Therefore, alternative study designs are desirable. The
                      design presented here consists of setting up a prediction
                      model for determining treatment effects under the control
                      condition for future patients. When a new treatment is
                      intended to be tested against a control treatment, a
                      single-arm trial for the new therapy is conducted. The
                      treatment effect is then evaluated by comparing the outcomes
                      of the single-arm trial against the predicted outcomes under
                      the control condition. While there are obvious advantages of
                      this design compared to classical RCTs (increased
                      efficiency, lower cost, alleviating participants' fear of
                      being on control treatment), there are several sources of
                      bias. Our aim is to investigate whether and how such a
                      design-the prediction design-may be used to provide
                      information on treatment effects by leveraging external data
                      sources. For this purpose, we investigated under what
                      assumptions linear prediction models could be used to
                      predict the counterfactual of patients precisely enough to
                      construct a test and an appropriate sample size formula for
                      evaluating the average treatment effect in the population of
                      a new study. A user-friendly R Shiny application (available
                      at: https://web.imbi.uni-heidelberg.de/PredictionDesignR/)
                      facilitates the application of the proposed methods, while a
                      real-world application example illustrates them.},
      keywords     = {counterfactual (Other) / historical data (Other) / linear
                      regression (Other) / prediction (Other) / real-world data
                      (Other)},
      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:37493036},
      doi          = {10.1002/bimj.202200023},
      url          = {https://inrepo02.dkfz.de/record/277785},
}