Department of Information and Computer Technology, Graduate School of Engineering, Tokyo University of Science, Tokyo, Japan.
Global Biometrics and Data Science, Bristol Myers Squibb K.K, Tokyo, Japan.
Biometrics. 2022 Dec;78(4):1651-1661. doi: 10.1111/biom.13510. Epub 2021 Aug 1.
Identification of the maximum tolerated dose combination (MTDC) of cancer drugs is an important objective in phase I oncology trials. Numerous dose-finding designs for drug combination have been proposed over the years. Copula-type models exhibit distinctive advantages in this task over other models used in existing competitive designs. For example, their application enables the consideration of dose-limiting toxicities attributable to one of two agents. However, if a particular combination therapy demonstrates extremely synergistic toxicity, copula-type models are liable to induce biases in toxicity probability estimators due to the associated Fréchet-Hoeffding bounds. Consequently, the dose-finding performance may be worse than those of other competitive designs. The objective of this study is to improve the performance of dose-finding designs based on copula-type models while maintaining their advantageous properties. We propose an extension of the parameter space of the interaction term in copula-type models. This releases the Fréchet-Hoeffding bounds, making the estimation of toxicity probabilities more flexible. Numerical examples in various scenarios demonstrate that the performance (e.g., the percentage of correct MTDC selection) of the proposed method is better than those exhibited by existing copula-type models and comparable with those of other competitive designs, irrespective of the existence of extreme synergistic toxicity. The results obtained in this study could motivate the real-world application of the proposed method in cases requiring the utilization of the properties of copula-type models.
确定癌症药物的最大耐受剂量组合(MTDC)是肿瘤学 I 期试验的一个重要目标。多年来已经提出了许多用于药物组合的剂量发现设计。在这项任务中,与现有竞争设计中使用的其他模型相比,Copula 型模型具有独特的优势。例如,它们的应用可以考虑归因于两种药物之一的剂量限制毒性。然而,如果特定的联合治疗表现出极其协同的毒性,Copula 型模型由于相关的 Fréchet-Hoeffding 边界,可能会导致毒性概率估计器产生偏差。因此,剂量发现性能可能会比其他竞争设计差。本研究的目的是在保持有利特性的同时,提高基于 Copula 型模型的剂量发现设计的性能。我们提出了扩展 Copula 型模型中交互项参数空间的方法。这释放了 Fréchet-Hoeffding 边界,使毒性概率的估计更加灵活。在各种情况下的数值示例表明,无论是否存在极端协同毒性,所提出方法的性能(例如,正确选择 MTDC 的百分比)都优于现有 Copula 型模型和其他竞争设计的性能。本研究的结果可能会激励在需要利用 Copula 型模型特性的情况下,将所提出的方法实际应用于现实世界。