Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Novartis Institutes for BioMedical Research, Cambridge, Massachusetts, USA.
Pharm Stat. 2021 Nov;20(6):1017-1034. doi: 10.1002/pst.2121. Epub 2021 Apr 1.
Incorporating historical data has a great potential to improve the efficiency of phase I clinical trials and to accelerate drug development. For model-based designs, such as the continuous reassessment method (CRM), this can be conveniently carried out by specifying a "skeleton," that is, the prior estimate of dose limiting toxicity (DLT) probability at each dose. In contrast, little work has been done to incorporate historical data into model-assisted designs, such as the Bayesian optimal interval (BOIN), Keyboard, and modified toxicity probability interval (mTPI) designs. This has led to the misconception that model-assisted designs cannot incorporate prior information. In this paper, we propose a unified framework that allows for incorporating historical data into model-assisted designs. The proposed approach uses the well-established "skeleton" approach, combined with the concept of prior effective sample size, thus it is easy to understand and use. More importantly, our approach maintains the hallmark of model-assisted designs: simplicity-the dose escalation/de-escalation rule can be tabulated prior to the trial conduct. Extensive simulation studies show that the proposed method can effectively incorporate prior information to improve the operating characteristics of model-assisted designs, similarly to model-based designs.
将历史数据纳入其中具有提高 I 期临床试验效率和加速药物开发的巨大潜力。对于基于模型的设计，例如连续评估方法 (CRM)，这可以通过指定“骨架”来方便地进行，即每个剂量的剂量限制毒性 (DLT) 概率的先验估计。相比之下，很少有工作将历史数据纳入模型辅助设计，例如贝叶斯最佳区间 (BOIN)、键盘和修改的毒性概率区间 (mTPI) 设计。这导致了一种误解，即模型辅助设计不能纳入先验信息。在本文中，我们提出了一个统一的框架，允许将历史数据纳入模型辅助设计。所提出的方法使用了成熟的“骨架”方法，结合了先验有效样本量的概念，因此易于理解和使用。更重要的是，我们的方法保持了模型辅助设计的标志：简单性-剂量递增/递减规则可以在试验进行之前制表。广泛的模拟研究表明，所提出的方法可以有效地纳入先验信息，以提高模型辅助设计的操作特性，类似于基于模型的设计。
