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

arXiv: https://arxiv.org/abs/1903.07082 · HTML: https://mlsanity.com/paper/1903.07082/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Mar 2019.

## Built on by (most cited first, 30 of 32)

- [Best Arm Identification with Safety Constraints](https://mlsanity.com/paper/2111.12151/) (2021)
- [Sliding-Window Thompson Sampling for Non-Stationary Settings](https://mlsanity.com/paper/2409.05181/) (2024)
- [Efficient Online Bayesian Inference for Neural Bandits](https://mlsanity.com/paper/2112.00195/) (2021)
- [Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Risk](https://mlsanity.com/paper/2204.00706/) (2022)
- [Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewards](https://mlsanity.com/paper/2506.16658/) (2025)
- [Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook](https://mlsanity.com/paper/2210.13623/) (2022)
- [Pure Exploration in Bandits with Linear Constraints](https://mlsanity.com/paper/2306.12774/) (2023)
- [Multi-disciplinary fairness considerations in machine learning for clinical trials](https://mlsanity.com/paper/2205.08875/) (2022)
- [Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks](https://mlsanity.com/paper/2210.12669/) (2022)
- [On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence](https://mlsanity.com/paper/2309.02202/) (2023)
- [ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine](https://mlsanity.com/paper/2111.13415/) (2021)
- [The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Bandits](https://mlsanity.com/paper/2309.03145/) (2023)
- [The Survival Bandit Problem](https://mlsanity.com/paper/2206.03019/) (2022)
- [Debiasing Samples from Online Learning Using Bootstrap](https://mlsanity.com/paper/2108.00236/) (2021)
- [Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis](https://mlsanity.com/paper/2401.11565/) (2024)
- [Graph Feedback Bandits with Similar Arms](https://mlsanity.com/paper/2405.11171/) (2024)
- [Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting](https://mlsanity.com/paper/2501.16018/) (2025)
- [Safe Linear Leveling Bandits](https://mlsanity.com/paper/2112.06728/) (2021)
- [Benefits of Monotonicity in Safe Exploration with Gaussian Processes](https://mlsanity.com/paper/2211.01561/) (2022)
- [Federated Learning for Heterogeneous Bandits with Unobserved Contexts](https://mlsanity.com/paper/2303.17043/) (2023)
- [Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewards](https://mlsanity.com/paper/2307.14138/) (2023)
- [Indirect Query Bayesian Optimization with Integrated Feedback](https://mlsanity.com/paper/2412.13559/) (2024)
- [LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attention](https://mlsanity.com/paper/2503.00387/) (2025)
- [COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agents](https://mlsanity.com/paper/2505.23720/) (2025)
- [AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities](https://mlsanity.com/paper/2311.18725/) (2023)
- [Privacy-Preserving UCB Decision Process Verification via zk-SNARKs](https://mlsanity.com/paper/2404.12186/) (2024)
- [Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraints](https://mlsanity.com/paper/2410.18844/) (2024)
- [Multi-armed Bandits with Missing Outcome](https://mlsanity.com/paper/2411.05661/) (2024)
- [Constrained Linear Thompson Sampling](https://mlsanity.com/paper/2503.02043/) (2025)
- [Cost-Aware Optimal Pairwise Pure Exploration](https://mlsanity.com/paper/2503.07877/) (2025)

## Abstract (arXiv)

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.