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Bayesian Enrichment Strategies for Randomized Discontinuation Trials

Authors

  • Lorenzo Trippa,

    Corresponding author
    1. Harvard School of Public Health and Department of Biostatistics, Dana–Farber Cancer Institute, Boston, Massachusetts 02115, U.S.A.
      email: lorenzo.trippa@jimmi.harvard.com
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  • Gary L. Rosner,

    Corresponding author
    1. Division of Biostatistics & Bioinformatics, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, Maryland 21205, U.S.A.
      email: grosner@jhmi.edu
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  • Peter Müller

    Corresponding author
    1. Department of Biostatistics, University of Texas M. D. Anderson Cancer Center, Houston, Texas 77030, U.S.A.
      email: pmueller@mdanderson.org
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email:lorenzo.trippa@jimmi.harvard.com

email:grosner@jhmi.edu

email:pmueller@mdanderson.org

Abstract

Summary We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.

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