论文标题
在I期试验中进行多个治疗课程的I期试验中的早期剂量调查的贝叶斯模型
Bayesian model for early dose-finding in phase I trials with multiple treatment courses
论文作者
论文摘要
肿瘤学的剂量调查临床试验旨在确定新药的最大耐受剂量（MTD），通常由短期剂量限制毒性（DLTS）的患者比例定义。基于模型的I期肿瘤学试验的方法已广泛设计，并且大多仅限于在治疗的第一个周期中发生的DLT，尽管患者继续接受多个周期的治疗。我们的目的是通过贝叶斯累积建模方法估算DLT对治疗周期序列的可能性，在此，DLT的概率被建模，并考虑到施用的药物的累积效应和发生的DLT周期。我们根据先前观察到的毒性提出了一种称为骰子（剂量发现累积）的设计，用于剂量升级和降低，旨在找到MTD序列（MTS）。我们进行了一项广泛的仿真研究，将这种方法与事件时间持续重新评估方法（Tite-CRM）和基准测试进行了比较。通常，我们的方法实现了正确或可比的正确MTS选择百分比。此外，我们研究了骰子预测能力。
Dose-finding clinical trials in oncology aim to determine the maximum tolerated dose (MTD) of a new drug, generally defined by the proportion of patients with short-term dose-limiting toxicities (DLTs). Model-based approaches for such phase I oncology trials have been widely designed and are mostly restricted to the DLTs occurring during the first cycle of treatment, although patients continue to receive treatment for multiple cycles. We aim to estimate the probability of DLTs over sequences of treatment cycles via a Bayesian cumulative modeling approach, where the probability of DLT is modeled taking into account the cumulative effect of the administered drug and the DLT cycle of occurrence. We propose a design, called DICE (Dose-fInding CumulativE), for dose escalation and de-escalation according to previously observed toxicities, which aims at finding the MTD sequence (MTS). We performed an extensive simulation study comparing this approach to the time-to-event continual reassessment method (TITE-CRM) and to a benchmark. In general, our approach achieved a better or comparable percentage of correct MTS selection. Moreover, we investigated the DICE prediction ability.
