# SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups

arXiv: https://arxiv.org/abs/2101.10998 · HTML: https://mlsanity.com/paper/2101.10998/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 44 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges](https://mlsanity.com/paper/1507.08025/) (2015)
- [A Contextual-bandit-based Approach for Informed Decision-making in Clinical Trials](https://mlsanity.com/paper/1809.00258/) (2018)
- [Thresholding Bandit for Dose-ranging: The Impact of Monotonicity](https://mlsanity.com/paper/1711.04454/) (2017)
- [Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints](https://mlsanity.com/paper/2006.05026/) (2020)
- [Contextual Constrained Learning for Dose-Finding Clinical Trials](https://mlsanity.com/paper/2001.02463/) (2020)
- [Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials](https://mlsanity.com/paper/1903.01026/) (2019)

## Built on by (most cited first, 6 of 6)

- [Federated Multi-Armed Bandits](https://mlsanity.com/paper/2101.12204/) (2021)
- [Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learning](https://mlsanity.com/paper/2103.01422/) (2021)
- [Multi-disciplinary fairness considerations in machine learning for clinical trials](https://mlsanity.com/paper/2205.08875/) (2022)
- [ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine](https://mlsanity.com/paper/2111.13415/) (2021)
- [Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and Solutions](https://mlsanity.com/paper/2503.09226/) (2025)
- [Adaptive Experiment Design with Synthetic Controls](https://mlsanity.com/paper/2401.17205/) (2024)

## Abstract (arXiv)

Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when multiple-drug dose-combinations (DC) are involved, due to the inherent conflict between the exponentially increasing DC candidates and the limited patient budget. This paper proposes a novel Bayesian design, SDF-Bayes, for finding the MTD for drug combinations in the presence of safety constraints. Rather than the conventional principle of escalating or de-escalating the current dose of one drug (perhaps alternating between drugs), SDF-Bayes proceeds by cautious optimism: it chooses the next DC that, on the basis of current information, is most likely to be the MTD (optimism), subject to the constraint that it only chooses DCs that have a high probability of being safe (caution). We also propose an extension, SDF-Bayes-AR, that accounts for patient heterogeneity and enables heterogeneous patient recruitment. Extensive experiments based on both synthetic and real-world datasets demonstrate the advantages of SDF-Bayes over state of the art DC trial designs in terms of accuracy and safety.