On Multi-Armed Bandit Designs for Dose-Finding Trials
Abstract
We study the problem of finding the optimal dosage in early stage clinical trials through the multi-armed bandit lens. We advocate the use of the Thompson Sampling principle, a flexible algorithm that can accommodate different types of monotonicity assumptions on the toxicity and efficacy of the doses. For the simplest version of Thompson Sampling, based on a uniform prior distribution for each dose, we provide finite-time upper bounds on the number of sub-optimal dose selections, which is unprecedented for dose-finding algorithms. Through a large simulation study, we then show that variants of Thompson Sampling based on more sophisticated prior distributions outperform state-of-the-art dose identification algorithms in different types of dose-finding studies that occur in phase I or phase I/II trials.
Domains
Machine Learning [stat.ML]
Fichier principal
AKR_ClinicalTrials20.pdf (445.42 Ko)
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doses.pdf (10.98 Ko)
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doses2.pdf (10.97 Ko)
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illustration.pdf (8.86 Ko)
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| Origin | Files produced by the author(s) |
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| Origin | Files produced by the author(s) |
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