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Activity Number: 359 - Contributed Poster Presentations: Biopharmacutical Section
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract #328927
Title: Clinical Trial Design Comparison with Covariate-Adjusted and Response Adaptive Randomization
Author(s): Wei Qiao* and Xuelin Huang and Jing Ning
Companies: The University of Texas M.D. Anderson and University of Texas MD Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center
Keywords: adaptive randomization; clinical trial designs; superiority confidence

Various Bayesian adaptive randomization (AR) methods have been proposed to assist clinicians in tailoring treatments. In order to assess the performance of the different designs, researchers usually compare each active treatment with a common control. However, when the covariate is a predictive factor, the comparison of different designs becomes difficult. The best treatment differs across individuals makes defining the power of a trial difficult. Our solution was to look at two criteria: the probability of selecting each individual's own best treatment by the final model and the proportion of individuals who received their best treatment during the trial. These two criteria are not used by previous researchers. Therefore, we conducted simulations to evaluate the performance of different allocation methods under Bayesian framework: AR by response rate; AR by superiority confidence; AR by covariate-adjusted response rate; AR by covariate-adjusted superiority confidence. Under four scenarios, in terms of the total number of responses and proportion of getting the best treatment, AR by covariate-adjusted superiority confidence outperformed the other allocation methods.

Authors who are presenting talks have a * after their name.

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