用水平集估计法精准找癌症试验最大耐受剂量，更安全更准。
Dose-finding design based on level set estimation in phase I cancer clinical trials
- 将最大耐受剂量问题转化为水平集估计，利用后验不确定性和过量风险控制选择下一剂量。
- 模拟显示新方法对MTD估计更准确，且过度给药风险更低。
- 适合临床试验设计者，尤其关注安全性与精准度的药物研发团队。
I期癌症临床试验的主要目标是评估新疗法的安全性并确定最大耐受剂量（MTD）。本文表明，MTD估计问题可视为水平集估计（LSE）问题，其目标是确定未知函数值高于或低于某阈值的区域。为此，提出一种基于LSE框架的新剂量寻找设计。该设计根据包含剂量-毒性曲线后验不确定性及过量控制的采集函数决定下一剂量。模拟实验显示，所提LSE设计在估计MTD方面具有更高准确性，且相比现有设计降低了过度给药风险，表明其为I期癌症临床试验设计提供了一种有效方法。
原文摘要 · Abstract (English)
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.
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