Time-to-event continual reassessment method incorporating treatment cycle information with application to an oncology phase I trial
Time-to-event continual reassessment method incorporating treatment cycle information with application to an oncology phase I trial
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DOI：
10.1002/bimj.201300261
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发表时间：
2014-11-01
影响因子：
1.7
通讯作者：
Kuan, Pei Fen
中科院分区：
文献类型：
--
作者：
Huang, Bo;Kuan, Pei Fen
延迟剂量限制性毒性（即超出第一个治疗周期）是 I 期试验的一个挑战。事件发生时间持续再评估方法（TITE-CRM）是一种贝叶斯剂量探索设计，旨在解决观察时间长和患者过早退出的问题。它使用加权二项式似然法，通过未知的毒性时间分布将权重分配给观察结果，并且可以持续累积。为了避免剂量过高，同时保持涉及多个周期的 DLT 评估的准确性和效率，我们提出了一种自适应权重函数，将实验治疗的循环数据与不断更新的参数相结合。这通过考虑周期间变异性提供了对毒性时间分布的合理估计，并保持了一致性和连贯性的统计特性。使用所提出的设计介绍了实验生物制剂的首次人体癌症试验的案例研究。对临床和统计参数进行设计校准，以确保良好的操作特性。仿真结果表明，所提出的具有自适应权重函数的 TITE-CRM 设计可显着缩短试验持续时间，不会使患者面临额外的风险，与现有的权重方法相比具有竞争力，并且具有一些理想的特性。
Delayed dose limiting toxicities (i.e. beyond first cycle of treatment) is a challenge for phase I trials. The time-to-event continual reassessment method (TITE-CRM) is a Bayesian dose-finding design to address the issue of long observation time and early patient drop-out. It uses a weighted binomial likelihood with weights assigned to observations by the unknown time-to-toxicity distribution, and is open to accrual continually. To avoid dosing at overly toxic levels while retaining accuracy and efficiency for DLT evaluation that involves multiple cycles, we propose an adaptive weight function by incorporating cyclical data of the experimental treatment with parameters updated continually. This provides a reasonable estimate for the time-to-toxicity distribution by accounting for inter-cycle variability and maintains the statistical properties of consistency and coherence. A case study of a First-in-Human trial in cancer for an experimental biologic is presented using the proposed design. Design calibrations for the clinical and statistical parameters are conducted to ensure good operating characteristics. Simulation results show that the proposed TITE-CRM design with adaptive weight function yields significantly shorter trial duration, does not expose patients to additional risk, is competitive against the existing weighting methods, and possesses some desirable properties.