Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints

TL;DR

This paper introduces SEEDA, a novel adaptive dose allocation method for Phase I clinical trials that maximizes efficacy while ensuring safety, outperforming existing methods in success rate and sample efficiency.

Contribution

The paper proposes SEEDA, a new adaptive trial design that balances efficacy maximization with safety constraints, including an extension tailored for plateau efficacy behaviors.

Findings

SEEDA achieves higher success rates in dose finding.

SEEDA uses fewer patients compared to existing methods.

The extended SEEDA-Plateau performs well with plateau efficacy profiles.

Abstract

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.