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.

中文翻译：

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联合用药临床试验中灵活运用Copula型剂量探索模型


确定抗癌药物的最大耐受剂量组合（MTDC）是 I 期肿瘤学试验的一个重要目标。多年来，已经提出了许多药物组合的剂量探索设计。与现有竞争设计中使用的其他模型相比，Copula 类型模型在此任务中表现出独特的优势。例如，它们的应用使得能够考虑归因于两种药物之一的剂量限制毒性。然而，如果特定的联合疗法表现出极其协同的毒性，则由于相关的 Fréchet-Hoeffding 界限，Copula 型模型很容易引起毒性概率估计量的偏差。因此，剂量探索性能可能比其他竞争设计更差。本研究的目的是提高基于 copula 型模型的剂量探索设计的性能，同时保持其优势特性。我们提出了 copula 型模型中交互项参数空间的扩展。这释放了 Fréchet-Hoeffding 界限，使毒性概率的估计更加灵活。各种场景中的数值示例表明，无论是否存在极端协同，所提出的方法的性能（例如，正确的 MTDC 选择的百分比）都优于现有的 copula 型模型，并且与其他竞争设计的性能相当。毒性。本研究获得的结果可以促进所提出的方法在需要利用 copula 类型模型属性的情况下的实际应用。