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Activity Number: 133
Type: Contributed
Date/Time: Monday, August 1, 2016 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract #318628
Title: Bayesian Response-Adaptive Covariate-Adjusted Randomization Design for Clinical Trials
Author(s): Jianchang Lin* and LiAn Lin and Serap Sankoh and Fang Yang and Guohui Liu
Companies: Takeda and Merck Research Laboratories and Takeda and Vertex Pharmaceuticals and Takeda
Keywords: Adaptive Design ; Clinical Trials ; Bayesian Adaptive Design
Abstract:

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency, which also have the advantage of assigning fewer patients to inferior treatment arms. However, these designs lack a mechanism to actively control the imbalance of prognostic factors, i.e. covariates that substantially affect the study outcome. Improving the balance of patient characteristics among the treatment arms could potentially increases the statistical power of the trial. We propose a randomization procedure that is response-adaptive and that also actively balances the covariates across treatment arms. We then incorporate this method into a sequential RA randomization design such that the resulting design skews the allocation probability to the better treatment arm, and also controls the imbalance of the prognostic factors across the arms. The proposed method extends the existing randomization where Ning and Huang (2010)'s approach requires polytomizing continuous covariates and Yuan et al. (2011)'s approach uses fixed allocation probability to adjust covariates imbalance.


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

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