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

Guosheng Yin (), Nan Chen () and J. Jack Lee ()
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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

Statistics in Biosciences, 2018, vol. 10, issue 2, No 9, 420-438

Abstract: 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.

Keywords: Bayesian adaptive design; Clinical trial; Power; Randomization; Survival data; Type I error; Type II error (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s12561-017-9199-7

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