Statistics and Decision Sciences Japan, Janssen Pharmaceutical K.K., Tokyo, Japan.
Department of Industrial Administration, Tokyo University of Science, Tokyo, Japan.
Pharm Stat. 2022 Nov;21(6):1324-1341. doi: 10.1002/pst.2247. Epub 2022 Jul 14.
Recently, model-assisted designs, including the Bayesian optimal interval (BOIN) design with optimal thresholds to determine the dose for the next cohort, have been proposed for cancer phase I studies. Model-assisted designs are useful because of their good performance as model-based designs in addition to their algorithm-based simplicity. In BOIN, escalation and de-escalation based on boundaries can be understood as a type of change point detection based on a sequential test procedure. Notably, the sequential test procedure is used in a wide range of fields and is known for its application to control charts, statistical monitoring methods used for detecting abnormalities in manufacturing processes. In control charts, abnormalities are detected if the control chart statistics are observed to be outside of the optimal boundaries. The cumulative sum (CUSUM) statistic, which is developed for control chart applications, derives higher power under the same erroneous judgment rate. Hence, it is expected that a more efficient model-assisted design can be achieved by the application of CUSUM statistics. In this study, a model-assisted design based on the CUSUM statistic is proposed. In the proposed design, the dose for the next cohort is decided by CUSUM statistics calculated from the counts of the dose-limiting toxicity and pre-defined boundaries, based on the CUSUM control chart scheme. Intensive simulation shows that our proposed method performs better than BOIN, and other representative model-assisted designs, including modified toxicity probability interval (mTPI) and Keyboard, in terms of controlling over-dosing rates while maintaining similar performance in the determination of maximum tolerated dose.
最近，已经提出了一些模型辅助设计，包括贝叶斯最优区间 (BOIN) 设计，该设计具有确定下一个队列剂量的最优阈值，可用于癌症 I 期研究。模型辅助设计很有用，因为它们除了算法简单之外，作为基于模型的设计也具有良好的性能。在 BOIN 中，基于边界的递增和递减可以理解为一种基于序贯检验过程的变化点检测。值得注意的是，序贯检验过程被广泛应用于多个领域，并且以其在控制图中的应用而闻名，控制图是用于检测制造过程中异常的统计监测方法。在控制图中，如果观察到控制图统计数据超出最优边界，则会检测到异常。累积和 (CUSUM) 统计量是为控制图应用而开发的，在相同错误判断率下具有更高的功效。因此，预计通过应用 CUSUM 统计量可以实现更有效的模型辅助设计。在本研究中，提出了一种基于 CUSUM 统计量的模型辅助设计。在提出的设计中，根据 CUSUM 控制图方案，从剂量限制毒性和预定义边界的计数中计算 CUSUM 统计量，以此来确定下一个队列的剂量。强化模拟表明，我们提出的方法在控制过度剂量率方面优于 BOIN 和其他代表性的模型辅助设计，包括改良毒性概率区间 (mTPI) 和 Keyboard，同时在确定最大耐受剂量方面保持相似的性能。
