A Bayesian Response-adaptive Covariate-adjusted Randomization Designfor Clinical Trials

Journal of Biometrics & Biostatistics

article

Jianchang Lin¹  Li-An Lin²  Serap Sankoh¹ 

[1] Takeda Pharmaceutical Company Limited;Merck Research Laboratories, Whitehouse Station

关键词: Adaptive design;    Clinical trials;    Bayesian adaptive design;   

DOI  :  10.4172/2155-6180.1000287

来源: Hilaris Publisher

【 摘 要 】

Accordingly to FDA draft guidance (2010), adaptive randomization (e.g. response-adaptive (RA) randomization) has become popular in clinical research because of its flexibility and efficiency,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) approach requires polytomizing continuous covariates and Yuan (2011) approach uses fixed allocation probability to adjust covariates imbalance.

【 授权许可】

Unknown