# Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

arXiv: https://arxiv.org/abs/2006.05026 · HTML: https://mlsanity.com/paper/2006.05026/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 13 of 13)

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- [Federated Multi-Armed Bandits Under Byzantine Attacks](https://mlsanity.com/paper/2205.04134/) (2022)
- [ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine](https://mlsanity.com/paper/2111.13415/) (2021)
- [Adaptive Experiment Design with Synthetic Controls](https://mlsanity.com/paper/2401.17205/) (2024)
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- [Truncated LinUCB for Stochastic Linear Bandits](https://mlsanity.com/paper/2202.11735/) (2022)
- [Benefits of Monotonicity in Safe Exploration with Gaussian Processes](https://mlsanity.com/paper/2211.01561/) (2022)
- [AI in Pharma for Personalized Sequential Decision-Making: Methods, Applications and Opportunities](https://mlsanity.com/paper/2311.18725/) (2023)
- [A Domain-Based Taxonomy of Jailbreak Vulnerabilities in Large Language Models](https://mlsanity.com/paper/2504.04976/) (2025)

## Abstract (arXiv)

Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite this, most commonly used methods in practice focus on identifying a Maximum Tolerated Dose (MTD) by learning only from toxicity events. We present a novel adaptive clinical trial methodology, called Safe Efficacy Exploration Dose Allocation (SEEDA), that aims at maximizing the cumulative efficacies while satisfying the toxicity safety constraint with high probability. We evaluate performance objectives that have operational meanings in practical clinical trials, including cumulative efficacy, recommendation/allocation success probabilities, toxicity violation probability, and sample efficiency. An extended SEEDA-Plateau algorithm that is tailored for the increase-then-plateau efficacy behavior of molecularly targeted agents (MTA) is also presented. Through numerical experiments using both synthetic and real-world datasets, we show that SEEDA outperforms state-of-the-art clinical trial designs by finding the optimal dose with higher success rate and fewer patients.