论文标题
在具有安全限制的自适应临床试验中学习剂量分配
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
论文作者
论文摘要
随着新化合物的功效与毒性之间的关系（或它们的组合）之间的关系变得更加复杂，I期剂量调查试验越来越具有挑战性。尽管如此，实际上，最常用的方法专注于仅通过从毒性事件中学习来识别最大耐受剂量（MTD）。我们提出了一种新型的自适应临床试验方法，称为安全疗效剂量分配（SEEDA），旨在最大程度地提高累积效率，同时以很高的可能性满足毒性安全性约束。我们评估在实际临床试验中具有操作意义的绩效目标，包括累积疗效，建议/分配成功概率，违反毒性的概率和样本效率。还介绍了一种针对分子靶向药物（MTA）的增强 - 斑点疗效行为而定制的扩展种子帕拉托算法。通过使用合成和现实世界数据集的数值实验，我们表明Seeda的表现优于最先进的临床试验设计，方法是找到更高的成功率和较少患者的最佳剂量。
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.
