Wages Nolan A, Conaway Mark R, O'Quigley John
Department of Mathematics and Computer Science, Hampden-Sydney College, Hampden-Sydney, Virginia 23943, USA.
Biometrics. 2011 Dec;67(4):1555-63. doi: 10.1111/j.1541-0420.2011.01560.x. Epub 2011 Mar 1.
Much of the statistical methodology underlying the experimental design of phase 1 trials in oncology is intended for studies involving a single cytotoxic agent. The goal of these studies is to estimate the maximally tolerated dose, the highest dose that can be administered with an acceptable level of toxicity. A fundamental assumption of these methods is monotonicity of the dose-toxicity curve. This is a reasonable assumption for single-agent trials in which the administration of greater doses of the agent can be expected to produce dose-limiting toxicities in increasing proportions of patients. When studying multiple agents, the assumption may not hold because the ordering of the toxicity probabilities could possibly be unknown for several of the available drug combinations. At the same time, some of the orderings are known and so we describe the whole situation as that of a partial ordering. In this article, we propose a new two-dimensional dose-finding method for multiple-agent trials that simplifies to the continual reassessment method (CRM), introduced by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48), when the ordering is fully known. This design enables us to relax the assumption of a monotonic dose-toxicity curve. We compare our approach and some simulation results to a CRM design in which the ordering is known as well as to other suggestions for partial orders.
肿瘤学中一期试验的实验设计所依据的许多统计方法,都是针对涉及单一细胞毒性药物的研究。这些研究的目标是估计最大耐受剂量,即能够在可接受的毒性水平下给药的最高剂量。这些方法的一个基本假设是剂量 - 毒性曲线的单调性。对于单药试验来说,这是一个合理的假设,因为预期给予更大剂量的药物会在越来越多的患者中产生剂量限制性毒性。在研究多种药物时,这个假设可能不成立,因为对于几种可用的药物组合,毒性概率的排序可能是未知的。与此同时,有些排序是已知的,所以我们将整个情况描述为部分排序的情况。在本文中,我们提出了一种用于多药试验的新的二维剂量寻找方法,当排序完全已知时,该方法简化为O'Quigley、Pepe和Fisher(1990年,《生物统计学》46卷,33 - 48页)提出的连续重新评估方法(CRM)。这种设计使我们能够放宽剂量 - 毒性曲线单调性的假设。我们将我们的方法和一些模拟结果与排序已知的CRM设计以及部分排序的其他建议进行了比较。