Mozgunov Pavel, Jaki Thomas, Paoletti Xavier
a Department of Mathematics and Statistics , Bailrigg, Lancaster , Lancaster University , UK.
b Service de Biostatistique et d'Epidémiologie & CESP OncoStat, INSERM , Institut Gustave Roussy, Université Paris-11 , Villejuif , France.
J Biopharm Stat. 2019;29(2):359-377. doi: 10.1080/10543406.2018.1535503. Epub 2018 Oct 23.
This work considers Phase I cancer dual-agent dose-escalation clinical trials in which one of the compounds is an immunotherapy. The distinguishing feature of trials considered is that the dose of one agent, referred to as a standard of care, is fixed and another agent is dose-escalated. Conventionally, the goal of a Phase I trial is to find the maximum tolerated combination (MTC). However, in trials involving an immunotherapy, it is also essential to test whether a difference in toxicities associated with the MTC and the standard of care alone is present. This information can give useful insights about the interaction of the compounds and can provide a quantification of the additional toxicity burden and therapeutic index. We show that both, testing for difference between toxicity risks and selecting MTC can be achieved using a Bayesian model-based dose-escalation design with two modifications. Firstly, the standard of care administrated alone is included in the trial as a control arm and each patient is randomized between the control arm and one of the combinations selected by a model-based design. Secondly, a flexible model is used to allow for toxicities at the MTC and the control arm to be modeled directly. We compare the performance of two-parameter and four-parameter logistic models with and without randomization to a current standard of such trials: a one-parameter model. It is found that at the cost of a small reduction in the proportion of correct selections in some scenarios, randomization provides a significant improvement in the ability to test for a difference in the toxicity risks. It also allows a better fitting of the combination-toxicity curve that leads to more reliable recommendations of the combination(s) to be studied in subsequent phases.
本研究考虑了I期癌症双药剂量递增临床试验，其中一种化合物为免疫疗法。所考虑试验的显著特征是，一种被称为标准治疗的药物剂量固定，另一种药物剂量递增。传统上，I期试验的目标是找到最大耐受组合（MTC）。然而，在涉及免疫疗法的试验中，测试与MTC和单独标准治疗相关的毒性差异是否存在也至关重要。这些信息可以提供有关化合物相互作用的有用见解，并可以量化额外的毒性负担和治疗指数。我们表明，使用基于贝叶斯模型的剂量递增设计并进行两处修改，就可以实现毒性风险差异测试和选择MTC这两个目标。首先，将单独给予的标准治疗作为一个对照臂纳入试验，并且将每位患者随机分配至对照臂和基于模型设计选择的组合之一。其次，使用灵活的模型直接对MTC和对照臂的毒性进行建模。我们将有随机化和无随机化的两参数和四参数逻辑模型的性能与此类试验的当前标准：单参数模型进行比较。结果发现，尽管在某些情况下正确选择比例会略有降低，但随机化在测试毒性风险差异的能力方面有显著提高。它还能更好地拟合组合毒性曲线，从而为后续阶段要研究的组合提供更可靠的建议。
