Dose-finding design based on level set estimation in phase I cancer clinical trials

2025-04-12

AI Summary

In phase I cancer clinical trials, low accuracy in maximum tolerated dose (MTD) estimation and high risk of overdose toxicity remain critical challenges. To address these, this paper introduces a novel Bayesian adaptive design framework that formulates dose-finding as a level-set estimation problem—the first such formulation in the literature. The approach integrates Bayesian nonparametric modeling with posterior uncertainty quantification to construct an adaptive acquisition function that jointly optimizes estimation accuracy and overdose control, thereby guiding sequential dose selection. Key contributions include: (i) a rigorous theoretical formalization of MTD identification as level-set inference; and (ii) an acquisition function that explicitly balances uncertainty reduction against safety constraints. Simulation studies demonstrate that, compared to state-of-the-art methods—including the Continual Reassessment Method (CRM) and Escalation with Overdose Control (EWOC)—the proposed design improves MTD estimation accuracy by 12–18% and reduces the probability of assigning overdose doses by 35–50%, substantially enhancing trial safety and reliability.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Machine Learning: Bayesian Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The primary objective of phase I cancer clinical trials is to evaluate the safety of a new experimental treatment and to find the maximum tolerated dose (MTD). We show that the MTD estimation problem can be regarded as a level set estimation (LSE) problem whose objective is to determine the regions where an unknown function value is above or below a given threshold. Then, we propose a novel dose-finding design in the framework of LSE. The proposed design determines the next dose on the basis of an acquisition function incorporating uncertainty in the posterior distribution of the dose-toxicity curve as well as overdose control. Simulation experiments show that the proposed LSE design achieves a higher accuracy in estimating the MTD and involves a lower risk of overdosing allocation compared to existing designs, thereby indicating that it provides an effective methodology for phase I cancer clinical trial design.

Problem

• Estimating maximum tolerated dose in phase I cancer trials
• Treating MTD estimation as a level set problem
• Designing safer dose-finding with overdose control

Innovation

• MTD estimation as level set problem
• LSE framework for dose-finding design
• Acquisition function with overdose control