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I期剂量探索性临床试验中联合用药的竞争设计

Competing designs for drug combination in phase I dose-finding clinical trials.

作者信息

Riviere M-K, Dubois F, Zohar S

机构信息

INSERM, U1138, Equipe 22, Centre de Recherche des Cordeliers, Université Paris 5, Université Paris 6, Paris, France; IRIS (Institut de Recherches Internationales Servier), Suresnes, France.

出版信息

Stat Med. 2015 Jan 15;34(1):1-12. doi: 10.1002/sim.6094. Epub 2014 Jan 27.

Abstract

The aim of phase I combination dose-finding studies in oncology is to estimate one or several maximum tolerated doses (MTDs) from a set of available dose levels of two or more agents. Combining several agents can indeed increase the overall anti-tumor action but at the same time also increase the toxicity. It is, however, unreasonable to assume the same dose-toxicity relationship for the combination as for the simple addition of each single agent because of a potential antagonist or synergistic effect. Therefore, using single-agent dose-finding methods for combination therapies is not appropriate. In recent years, several authors have proposed novel dose-finding designs for combination studies, which use either algorithm-based or model-based methods. The aim of our work was to compare, via a simulation study, six dose-finding methods for combinations proposed in recent years. We chose eight scenarios that differ in terms of the number and location of the true MTD(s) in the combination space. We then compared the performance of each design in terms of correct combination selection, patient allocation, and mean number of observed toxicities during the trials. Our results showed that the model-based methods performed better than the algorithm-based ones. However, none of the compared model-based designs gave consistently better results than the others.

摘要

肿瘤学中I期联合剂量探索研究的目的是从两种或更多药物的一组可用剂量水平中估计一个或几个最大耐受剂量(MTD)。联合使用几种药物确实可以增强整体抗肿瘤作用,但同时也会增加毒性。然而,由于可能存在的拮抗或协同效应,假设联合用药的剂量-毒性关系与每种单一药物简单相加时相同是不合理的。因此,将单药剂量探索方法用于联合治疗并不合适。近年来,一些作者提出了用于联合研究的新型剂量探索设计,这些设计使用基于算法或基于模型的方法。我们工作的目的是通过模拟研究比较近年来提出的六种联合用药剂量探索方法。我们选择了八个场景,这些场景在联合空间中真实MTD的数量和位置方面有所不同。然后,我们从正确的联合选择、患者分配以及试验期间观察到的毒性平均数量方面比较了每种设计的性能。我们的结果表明,基于模型的方法比基于算法的方法表现更好。然而,在比较的基于模型的设计中,没有一种始终比其他设计给出更好的结果。

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