A Markov decision process for response-adaptive randomization in clinical trials

David Merrell, Thevaa Chandereng and Yeonhee Park

Computational Statistics & Data Analysis, 2023, vol. 178, issue C

Abstract: In clinical trials, response-adaptive randomization (RAR) has the appealing ability to assign more subjects to better-performing treatments based on interim results. Traditional RAR strategies alter the randomization ratio on a patient-by-patient basis. An alternate approach is blocked RAR, which groups patients together in blocks and recomputes the randomization ratio in a block-wise fashion; past works show that this provides robustness against time-trend bias. However, blocked RAR poses additional questions: how many blocks should there be, and how many patients should each block contain?

Keywords: Clinical trials; Adaptive trials; Response-adaptive randomization; Reinforcement learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:178:y:2023:i:c:s0167947322001797

DOI: 10.1016/j.csda.2022.107599

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