A comparison of model choices for the continual reassessment method in phase I cancer trials
A comparison of model choices for the continual reassessment method in phase I cancer trials
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DOI：
10.1002/sim.3682
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发表时间：
2009-10-30
影响因子：
2
通讯作者：
Kramar, A.
中科院分区：
文献类型：
--
作者：
Paoletti, X.;Kramar, A.
确定最大耐受剂量（MTD）是I期试验的主要目的。试验通常在有限的样本量下进行。提出的用于确定MTD的基于模型的方法（包括连续重新评估方法或CRM）假设了剂量-毒性关系的简单模型。在临床开发的早期阶段，真正的模型家族尚不清楚，已经提出了几项建议。即使在模型误设定的情况下，也可以使用单参数模型获得建议到真实MTD的渐近收敛。然而，有限样本量的操作特征在很大程度上会受到模型选择的影响。本文在模拟框架中评估和比较了几个模型。这个框架包括一个大类的剂量-毒性关系，对竞争模型进行测试，一个“最佳”的方法，提供有效的非参数估计的剂量限制毒性的概率作为基准，并作为图形表示。特别是，我们探讨了使用一个参数与两个参数的模型，我们比较的权力和逻辑模型，最后我们调查的影响剂量重新编码的操作特性。进行比较与一个可能性的方法和贝叶斯模型估计的方法。我们表明，一个参数模型的平均性能是优越的上级和功率模型具有良好的操作特性。一些模型可以加速剂量递增，导致更积极的设计。我们推导出一些与模型选择相关的行为，并坚持在每次新试验开始前使用几种场景下的模拟，以确定要使用的最佳模型。版权所有（C）2009约翰威利父子有限公司
Determination of the maximum tolerated dose (MTD) is the main objective of phase I trials. Trials are typically carried out with restricted sample sizes. Model-based approaches proposed to identify the MTD (including the Continual Reassessment Method or CRM) suppose a simple model for the dose-toxicity relation. At this early stage of clinical development, the true family of models is not known and several proposals have been done. Asymptotic convergence of the recommendation to the true MTD can be obtained with a one-parameter model even in case of model misspecification. Nevertheless, operating characteristics with finite sample sizes can be largely affected by the choice of the model.In this paper, we evaluate and compare several models in a simulation framework. This framework includes a large class of dose-toxicity relations against which to test the competing models, an 'optimal' method that provides efficient non-parametric estimates of the probability of dose limiting toxicity to serve as a benchmark and as a graphic representation. In particular we explore the use of a one-parameter versus a two-parameter model, we compare the power and the logistic models and finally we investigate the impact of dose recoding on the operating characteristics. Comparisons are carried out with both a likelihood approach and a Bayesian approach for model estimations. We show that average performances of a one-parameter model are superior and that the power model has good operating characteristics. Some models can speed up dose escalation and lead to more aggressive designs.We derive some behavior related to the choice of model and insist on the use of simulations under several scenarios before the initiation of each new trial in order to determine the best model to be used. Copyright (C) 2009 John Wiley & Sons, Ltd.