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Phase Ⅰ-Ⅱ trial design for biologic agents using conditional auto-regressive models for toxicity and efficacy

机译:使用条件自回归模型的毒性和功效的生物制剂Ⅰ-Ⅱ期试验设计

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摘要

A traditional assumption in the design of chemotherapy phase Ⅰ-Ⅱ trial designs is that dose increases lead to both more toxicity as well as more efficacy. This assumption of monotonic rates of toxicity and efficacy has come into question as potential cancer treatments are less likely to be chemotherapy and are instead biologic agents. These biologic agents tend to have mechanisms of action that act as 'on-off' switches for cancer growth, so giving more of the biologic agents will not necessarily provide any more benefit (and possibly no further risk) to the patient. We propose the use of a conditional auto-regressive (CAR) model as a way to estimate adaptively the rates of dose limiting toxicities (DLTs) and efficacy by smoothing the data collected for all doses in such a way that allows for non-increasing rates of either outcome with dose. We present the study design for our CAR model approach and compare, via simulation, the operating characteristics of our design with two existing contemporary published approaches. We demonstrate that our CAR model approach is a viable design for an adaptive phase Ⅰ-Ⅱ trial that can accommodate a variety of toxicity-dose and efficacy-dose patterns.
机译:在化学疗法Ⅰ-Ⅱ期试验设计中,一个传统的假设是剂量增加会导致更大的毒性和更大的疗效。由于潜在的癌症治疗方法不太可能是化学疗法,而是生物制剂,因此这种单调毒性和功效率的假设已受到质疑。这些生物制剂倾向于具有充当癌症生长的“开关”开关的作用机制,因此给予更多的生物制剂不一定会给患者带来更多益处(并且可能没有进一步的风险)。我们建议使用条件自回归(CAR)模型,通过对所有剂量收集的数据进行平滑处理,以允许不增加发生率的方式,来自适应地估算剂量限制毒性(DLT)和功效的发生率与剂量的任何结果。我们介绍了我们的CAR模型方法的研究设计,并通过模拟将我们的设计的操作特性与两种现有的当代公开方法进行了比较。我们证明了我们的CAR模型方法是适用于适应性Ⅰ-Ⅱ期试验的可行设计,该试验可以适应各种毒性剂量和功效剂量模式。

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