A practical response adaptive block randomization (RABR) design with analytic type I error protection

Abstract

ical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. However, applications of RAR in confirmatory drug clinical trials with multiple active arms are limited largely due to its complexity, and lack of control of randomization ratios to different treatment groups. To address the aforementioned issues, we propose a Response Adaptive Block Randomization (RABR) design allowing arbitrarily prespecified randomization ratios for the control and high-performing groups to meet clinical trial objectives. We show the validity of the conventional unweighted test in RABR with a controlled type I error rate based on the weighted combination test for sample size adaptive design invoking no large sample approximation. The advantages of the proposed RABR in terms of robustly reaching target final sample size to meet regulatory requirements and increasing statistical power as compared with the popular Doubly Adaptive Biased Coin Design are demonstrated by statistical simulations and a practical clinical trial design example.

Model-based adaptive randomization procedures for heteroscedasticity of treatment responses

“…Response-adaptive randomization is appealing from methodological, ethical, and pragmatic perspectives in the sense that subjects are more likely to be randomized to better performing treatment groups based on accumulating data. 22 Since the two model-based adaptive randomization procedures proposed do not use information of the current patient when assigning him or her, they can fit into the framework of response-adaptive randomization. In this article, our focus is mainly on improving the power of tests.…”

Sample size adaptation designs and efficiency comparison with group sequential designs

“…6 This method, also referred to as the CHW method or CHW design, allows a mid-course change in the trial final sample size as well as the randomization ratio. [7][8][9][10][11][12] An equivalent method, also referred to as the "inverse normal method," was proposed by Lehmacher and Wassmer, 13 in which Z-statistics in the combination formula are expressed as inverses of P-values. All methods control the type I error rate.…”