A Generalized Continual Reassessment Method for Two-Agent Phase I Trials
A Generalized Continual Reassessment Method for Two-Agent Phase I Trials
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DOI：
10.1080/19466315.2013.767213
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发表时间：
2013-05-01
影响因子：
1.8
通讯作者：
Jia, Nan
中科院分区：
文献类型：
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作者：
Braun, Thomas M.;Jia, Nan
许多模型已被建议用于I期适应性设计，以确定两种药物的最大耐受组合（MTC）。然而，这些设计尚未被采用为实际临床试验中使用的标准方法，我们认为这主要是由于所使用的模型的复杂性。鉴于连续再评估方法（CRM）正逐渐被采用作为单药I期试验的标准，我们提出了一个广义版本的CRM，我们表示为gCRM，希望提供这样一个标准的两剂试验。对于一种药物的每一个剂量，我们应用传统的CRM来研究另一种药物的剂量;这些CRM设计中的每一个都假设相同的剂量-毒性模型，以及模型中使用的参数值。然而，每个模型包括第二个参数，该参数在模型之间变化，以在对所有组合的剂量限制性毒性（DLT）的概率建模时允许灵活性，但也在相邻组合之间借用强度。我们采用了自适应贝叶斯算法，依次将每例患者分配到最合适的剂量组合，并将患者分配集中到DLT概率最接近预定目标率的剂量组合。我们通过广泛的模拟在各种情况下，可能会出现在两个代理I期试验中测试我们的方法的性能。我们还直接将我们模型的操作特性与其他已发布模型进行比较。
Numerous models have been suggested for Phase I adaptive designs for identifying the maximum tolerated combination (MTC) of two agents. However, these designs have yet to be adopted as the standard approach to use in actual clinical trials, which we posit is mostly due to the complexity of the models that are used. Given that the continual reassessment method (CRM) is gradually being adopted as a standard for single-agent Phase I trials, we propose a generalized version of the CRM, which we denote by gCRM, in hopes of providing such a standard for two-agent trials. For each dose of one agent, we apply the traditional CRM to study doses of the other agent; each of these CRM designs assumes the same dose-toxicity model, as well as the value of the parameter used in the model. However, each model includes a second parameter that varies among the models in an effort to allow flexibility when modeling the probability of dose-limiting toxicity (DLT) of all combinations, yet borrow strength among neighboring combinations as well. We incorporate an adaptive Bayesian algorithm to sequentially assign each patient to the most appropriate dose combination, as well as focus patient assignments to a dose combination that has a DLT probability closest to a prespecified target rate. We test the performance of our method via extensive simulations in various scenarios that are likely to arise in two-agent Phase I trials. We also directly compare the operating characteristics of our model to the alternate published models.