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Activity Number: 332 - SPEED: Section on Bayesian Statistical Science
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #322966 View Presentation
Title: A Bayesian Sequential Design with Adaptive Randomization for Two-Sided Hypothesis Tests
Author(s): Qingzhao Yu* and Lin Zhu and Han Zhu
Companies: Louisiana State University Health Sciences Center and Louisiana State University Health Sciences Center and Pharmaceutical Product Development, Inc.
Keywords: Adaptive randomization rate ; Bayesian Clinical Trial ; Sequential Design
Abstract:

Bayesian sequential and adaptive randomization designs are gaining popularity in clinical trials thanks to their potentials to reduce the number of required participants and save resources. We propose a Bayesian sequential design with adaptive randomization rates to more efficiently attribute newly recruited patients to different treatment arms. Algorithms are presented to calculate the optimal randomization rate, critical values, and power for the proposed design. Sensitivity analysis is implemented to check the influence on design by changing the prior distributions. Simulation studies are applied to compare the proposed method and traditional methods in terms of power and actual sample sizes. Simulations show that, when total sample size is fixed, the proposed design can obtain greater power and/or cost smaller actual sample size than the traditional Bayesian sequential design. Finally, we apply the proposed method to a real data set and compare the results with the Bayesian sequential design without adaptive randomization in terms of sample sizes. The proposed method can further reduce required sample size.


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