Abstract
Response-Adaptive Randomization (RAR) is part of a wider class of data-dependent sampling algorithms, for which clinical trials are typically used as a motivating application. In that context, patient allocation to treatments is determined by randomization probabilities that change based on the accrued response data in order to achieve experimental goals. RAR has received abundant theoretical attention from the biostatistical literature since the 1930s and has been the subject of numerous debates. In the last decade, it has received renewed consideration from the applied and methodological communities, driven by well-known practical examples and its widespread use in machine learning. Papers on the subject present different views on its usefulness, and these are not easy to reconcile. This work aims to address this gap by providing a broad, balanced and fresh review of methodological and practical issues to consider when debating the use of RAR in clinical trials.
Funding Statement
The authors acknowledge funding and support from the UK Medical Research Council (grants MC_UU_00002/15 (SSV), MC_UU_00002/3 (BCL-K), MC_UU_00002/14 (DSR), MR/N028171/1 (KML)), the Biometrika Trust (DSR) and the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014) (DSR, KML, BCL-K, SSV). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health and Social Care (DHSC). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising.
Acknowledgments
The authors thank the Editor, Associate Editor and the four anonymous reviewers for their constructive comments which helped substantially improve this paper. We also thank Peter Jacko for many helpful comments on an earlier version of this work, Andi Zhang for providing code for the FLGI procedures used in Section 3.1, Ayon Mukherjee for suggesting the use of the drop-the-loser rule in Section 3.1, Arina Kazimianec for her work on sample size imbalance, which helped motivate Section 3.1, Nikolaos Skourlis for screening literature on model-based adaptive randomization, and Qingyuan Zhao and Ian White for helpful comments on Sections 1–3.
Citation
David S. Robertson. Kim May Lee. Boryana C. López-Kolkovska. Sofía S. Villar. "Response-Adaptive Randomization in Clinical Trials: From Myths to Practical Considerations." Statist. Sci. 38 (2) 185 - 208, May 2023. https://doi.org/10.1214/22-STS865
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