mlsanityData to 7 Oct 2026

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

arXiv 1903.07082 ↗First posted 17 Mar 2019stat.ML cs.LGcited by 32

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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.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Mar 2019.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 6; 2024: 7; 2025: 7; 2026: 1.

32 papers.

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  1. 2021Best Arm Identification with Safety Constraintscited by 25
  2. 2021Efficient Online Bayesian Inference for Neural Banditscited by 15
  3. 2024Sliding-Window Thompson Sampling for Non-Stationary Settingscited by 15
  4. 2022Strategies for Safe Multi-Armed Bandits with Logarithmic Regret and Riskcited by 14
  5. 2025Multi-Armed Bandits With Machine Learning-Generated Surrogate Rewardscited by 13
  6. 2022Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlookcited by 10
  7. 2023Pure Exploration in Bandits with Linear Constraintscited by 10
  8. 2022Multi-disciplinary fairness considerations in machine learning for clinical trialscited by 8
  9. 2022Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networkscited by 6
  10. 2023On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidencecited by 6
  11. 2021ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicinecited by 5
  12. 2023The Best Arm Evades: Near-optimal Multi-pass Streaming Lower Bounds for Pure Exploration in Multi-armed Banditscited by 5
20 more
  1. 2022The Survival Bandit Problemcited by 4
  2. 2021Debiasing Samples from Online Learning Using Bootstrapcited by 3
  3. 2024Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysiscited by 2
  4. 2024Graph Feedback Bandits with Similar Armscited by 2
  5. 2025Strategic Multi-Armed Bandit Problems Under Debt-Free Reportingcited by 2
  6. 2021Safe Linear Leveling Banditscited by 1
  7. 2022Benefits of Monotonicity in Safe Exploration with Gaussian Processescited by 1
  8. 2023Federated Learning for Heterogeneous Bandits with Unobserved Contextscited by 1
  9. 2023Piecewise-Stationary Combinatorial Semi-Bandit with Causally Related Rewardscited by 1
  10. 2024Indirect Query Bayesian Optimization with Integrated Feedbackcited by 1
  11. 2025LNUCB-TA: Linear-nonlinear Hybrid Bandit Learning with Temporal Attentioncited by 1
  12. 2025COBRA: Contextual Bandit Algorithm for Ensuring Truthful Strategic Agentscited by 1
  13. 2023AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunitiescited by 0
  14. 2024Privacy-Preserving UCB Decision Process Verification via zk-SNARKscited by 0
  15. 2024Learning to Explore with Lagrangians for Bandits under Unknown Linear Constraintscited by 0
  16. 2024Multi-armed Bandits with Missing Outcomecited by 0
  17. 2025Constrained Linear Thompson Samplingcited by 0
  18. 2025Cost-Aware Optimal Pairwise Pure Explorationcited by 0
  19. 2025Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Banditscited by 0
  20. 2026Safety by Design: Realized-Cost Constraints for Contextual Bandits with Continuous Actionscited by 0