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

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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.

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Acknowledgements

We thank the three referees for their constructive and insightful comments that led to significant improvements in the article. This research was supported in part by the Research Grants Council of Hong Kong (Grant 17326316), the U.S. National Institutes of Health (Grants CA16672 and CA97007), and the Cancer Prevention & Research Institute of Texas Multi-Investigator Research Award (CPRIT MIRA Grant RP160668).

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Authors and Affiliations

  1. Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam Road, Pokfulam, Hong Kong

    Guosheng Yin

  2. Department of Biostatistics, The University of Texas M. D. Anderson Cancer Center, Houston, TX, 77030, USA

    Nan Chen & J. Jack Lee

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  1. Guosheng Yin
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  2. Nan Chen
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  3. J. Jack Lee
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Correspondence to J. Jack Lee.

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Yin, G., Chen, N. & Lee, J.J. Bayesian Adaptive Randomization and Trial Monitoring with Predictive Probability for Time-to-Event Endpoint. Stat Biosci 10, 420–438 (2018). https://doi.org/10.1007/s12561-017-9199-7

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  • DOI: https://doi.org/10.1007/s12561-017-9199-7

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