Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Biostatistics. 2020 Oct 1;21(4):807-824. doi: 10.1093/biostatistics/kxz007.
Two useful strategies to speed up drug development are to increase the patient accrual rate and use novel adaptive designs. Unfortunately, these two strategies often conflict when the evaluation of the outcome cannot keep pace with the patient accrual rate and thus the interim data cannot be observed in time to make adaptive decisions. A similar logistic difficulty arises when the outcome is late-onset. Based on a novel formulation and approximation of the likelihood of the observed data, we propose a general methodology for model-assisted designs to handle toxicity data that are pending due to fast accrual or late-onset toxicity and facilitate seamless decision making in phase I dose-finding trials. The proposed time-to-event model-assisted designs consider each dose separately and the dose-escalation/de-escalation rules can be tabulated before the trial begins, which greatly simplifies trial conduct in practice compared to that under existing methods. We show that the proposed designs have desirable finite and large-sample properties and yield performance that is comparable to that of more complicated model-based designs. We provide user-friendly software for implementing the designs.
有两种有效的策略可以加快药物开发速度,分别是增加患者入组率和使用新型适应性设计。然而,当评估结果的速度赶不上患者入组率时,这两种策略往往会产生冲突,导致无法及时观察到中期数据来做出适应性决策。当结果为迟发性时,也会出现类似的逻辑困难。基于对观察数据似然函数的新公式和近似,我们提出了一种通用的方法来处理因快速入组或迟发性毒性而延迟的毒性数据,以便在 I 期剂量发现试验中进行无缝决策。所提出的基于时间的事件模型辅助设计分别考虑每个剂量,并且可以在试验开始之前制定剂量递增/递减规则,这与现有方法相比大大简化了实际试验的进行。我们表明,所提出的设计具有理想的有限和大样本性质,并产生与更复杂的基于模型的设计相当的性能。我们提供了易于使用的软件来实现这些设计。