Department of Mathematics and Computer Science, Hampden-Sydney College, Hampden-Sydney, Virginia 23943, USA.
Clin Trials. 2011 Aug;8(4):380-9. doi: 10.1177/1740774511408748. Epub 2011 Jun 7.
Most of the current designs used for Phase I dose finding trials in oncology will either involve only a single cytotoxic agent or will impose some implicit ordering among the doses. The goal of the studies is to estimate the maximum tolerated dose (MTD), the highest dose that can be administered with an acceptable level of toxicity. A key working assumption of these methods is the monotonicity of the dose-toxicity curve.
Here we consider situations in which the monotonicity assumption may fail. These studies are becoming increasingly common in practice, most notably, in phase I trials that involve combinations of agents. Our focus is on studies where there exist pairs of treatment combinations for which the ordering of the probabilities of a dose-limiting toxicity cannot be known a priori.
We describe a new dose-finding design which can be used for multiple-drug trials and can be applied to this kind of problem. Our methods proceed by laying out all possible orderings of toxicity probabilities that are consistent with the known orderings among treatment combinations and allowing the continual reassessment method (CRM) to provide efficient estimates of the MTD within these orders. The design can be seen to simplify to the CRM when the full ordering is known.
We study the properties of the design via simulations that provide comparisons to the Bayesian approach to partial orders (POCRM) of Wages, Conaway, and O'Quigley. The POCRM was shown to perform well when compared to other suggested methods for partial orders. Therefore, we comapre our approach to it in order to assess the performance of the new design.
A limitation concerns the number of possible orders. There are dose-finding studies with combinations of agents that can lead to a large number of possible orders. In this case, it may not be feasible to work with all possible orders.
The proposed design demonstrates the ability to effectively estimate MTD combinations in partially ordered dosefinding studies. Because it relaxes the monotonicity assumption, it can be considered a multivariate generalization of the CRM. Hence, it can serve as a link between single and multiple-agent dosefinding trials.
大多数当前用于肿瘤学 I 期剂量发现试验的设计要么只涉及单一细胞毒性药物，要么会对剂量施加某种隐含的顺序。这些研究的目标是估计最大耐受剂量（MTD），即可以给予可接受毒性水平的最高剂量。这些方法的一个关键工作假设是剂量-毒性曲线的单调性。
在这里，我们考虑单调性假设可能失败的情况。这些研究在实践中越来越常见，最值得注意的是，涉及药物组合的 I 期试验。我们的重点是研究存在两种治疗组合的情况，对于这两种组合，剂量限制毒性的概率排序是无法事先知道的。
我们描述了一种新的剂量发现设计，可用于多药物试验，并可应用于此类问题。我们的方法通过列出与已知治疗组合排序一致的所有可能的毒性概率排序，并允许持续再评估方法（CRM）在这些排序内提供 MTD 的有效估计，来进行操作。当完全排序已知时，该设计可以简化为 CRM。
我们通过模拟研究了设计的性质，这些模拟提供了与 Wages、Conaway 和 O'Quigley 的部分排序贝叶斯方法（POCRM）的比较。与其他部分排序方法相比，POCRM 表现良好。因此，我们将其与我们的方法进行比较，以评估新设计的性能。
一个局限性涉及可能的顺序数量。有一些组合药物的剂量发现研究可能导致大量可能的顺序。在这种情况下，可能无法对所有可能的顺序进行处理。
所提出的设计证明了在部分有序剂量发现研究中有效估计 MTD 组合的能力。由于它放宽了单调性假设，因此可以被视为 CRM 的多元概括。因此，它可以作为单药和多药剂量发现试验之间的联系。
