Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
This work addresses the challenge of optimizing dose allocation in clinical trials for new compounds, particularly molecularly targeted agents, which is crucial for improving patient outcomes and trial efficiency in medical research.
The authors tackled the problem of dose allocation in adaptive clinical trials with complex efficacy-toxicity relationships by introducing the SEEDA methodology, which maximizes cumulative efficacy while maintaining safety constraints, and demonstrated that it outperforms state-of-the-art designs with higher success rates and fewer patients in numerical experiments.
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.