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Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint

Author

Listed:
  • Guosheng Yin

    (The University of Hong Kong)

  • Nan Chen

    (The University of Texas M. D. Anderson Cancer Center)

  • J. Jack Lee

    (The University of Texas M. D. Anderson Cancer Center)

Abstract

There has been much development in Bayesian adaptive designs in clinical trials. In the Bayesian paradigm, the posterior predictive distribution characterizes the future possible outcomes given the currently observed data. Based on the interim time-to-event data, we develop a new phase II trial design by combining the strength of both Bayesian adaptive randomization and the predictive probability. By comparing the mean survival times between patients assigned to two treatment arms, more patients are assigned to the better treatment on the basis of adaptive randomization. We continuously monitor the trial using the predictive probability for early termination in the case of superiority or futility. We conduct extensive simulation studies to examine the operating characteristics of four designs: the proposed predictive probability adaptive randomization design, the predictive probability equal randomization design, the posterior probability adaptive randomization design, and the group sequential design. Adaptive randomization designs using predictive probability and posterior probability yield a longer overall median survival time than the group sequential design, but at the cost of a slightly larger sample size. The average sample size using the predictive probability method is generally smaller than that of the posterior probability design.

Suggested Citation

  • Guosheng Yin & Nan Chen & J. Jack Lee, 2018. "Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(2), pages 420-438, August.
  • Handle: RePEc:spr:stabio:v:10:y:2018:i:2:d:10.1007_s12561-017-9199-7
    DOI: 10.1007/s12561-017-9199-7
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    References listed on IDEAS

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    1. Yi Cheng & Donald A. Berry, 2007. "Optimal adaptive randomized designs for clinical trials," Biometrika, Biometrika Trust, vol. 94(3), pages 673-689.
    2. Ying Yuan & Guosheng Yin, 2009. "Bayesian dose finding by jointly modelling toxicity and efficacy as time‐to‐event outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(5), pages 719-736, December.
    3. Guosheng Yin & Nan Chen & J. Jack Lee, 2012. "Phase II trial design with Bayesian adaptive randomization and predictive probability," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(2), pages 219-235, March.
    4. Lanju Zhang & William F. Rosenberger, 2007. "Response‐adaptive randomization for survival trials: the parametric approach," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 56(2), pages 153-165, March.
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    Cited by:

    1. Valeria Sambucini, 2021. "Bayesian Sequential Monitoring of Single-Arm Trials: A Comparison of Futility Rules Based on Binary Data," IJERPH, MDPI, vol. 18(16), pages 1-17, August.

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