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Evaluating Bayesian adaptive randomization procedures with adaptive clip methods for multi-arm trials

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)1273-1287
Number of pages15
JournalStatistical Methods in Medical Research
Issue number5
PublishedMay 2021

Bibliographical note

Funding Information: We are grateful to the reviewers for their helpful comments on an earlier version of this paper. The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: This work and KML were funded by the Medical Research Council in the United Kingdom (grant codes MR/N028171/1). JJL?s research was supported in part by the grants CA016672 and CA221703 from the National Cancer Institute in the United States. Publisher Copyright: © The Author(s) 2021. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors


Bayesian adaptive randomization is a heuristic approach that aims to randomize more patients to the putatively superior arms based on the trend of the accrued data in a trial. Many statistical aspects of this approach have been explored and compared with other approaches; yet only a limited number of works has focused on improving its performance and providing guidance on its application to real trials. An undesirable property of this approach is that the procedure would randomize patients to an inferior arm in some circumstances, which has raised concerns in its application. Here, we propose an adaptive clip method to rectify the problem by incorporating a data-driven function to be used in conjunction with Bayesian adaptive randomization procedure. This function aims to minimize the chance of assigning patients to inferior arms during the early time of the trial. Moreover, we propose a utility approach to facilitate the selection of a randomization procedure. A cost that reflects the penalty of assigning patients to the inferior arm(s) in the trial is incorporated into our utility function along with all patients benefited from the trial, both within and beyond the trial. We illustrate the selection strategy for a wide range of scenarios.

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