On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials

TL;DR

This paper applies multi-armed bandit algorithms, specifically Thompson Sampling, to optimize dose-finding in early clinical trials, providing theoretical bounds and demonstrating superior performance over existing methods.

Contribution

It introduces a Thompson Sampling approach for dose-finding, offers finite-time bounds, and shows its effectiveness through extensive simulations.

Findings

Thompson Sampling outperforms existing dose-finding algorithms.

Finite-time bounds are established for the simplest Thompson Sampling variant.

Advanced priors improve dose identification accuracy.

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.