Model calibration in the continual reassessment method.
Model calibration in the continual reassessment method.
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DOI：
10.1177/1740774509105076
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发表时间：
2009-06
期刊：
影响因子：
--
通讯作者：
Ying Kuen Cheung
中科院分区：
文献类型：
--
作者：
Lee SM;Ying Kuen Cheung
连续再评估方法（CRM）是一种基于自适应模型的设计，用于在剂量探索临床试验中估计最大耐受剂量。评价给定CRM模型的灵敏度的一种方法是使用无差异区间，包括剂量-毒性曲线的函数形式、模型参数的先验分布以及每个剂量下毒性概率的初始猜测。虽然无差异区间技术提供了一个简洁的总结模型的敏感性，有无限多的可能的方式来指定的毒性概率的初始猜测。在实践中，这些通常是通过大量的模拟试验和错误来指定的。通过使用无差异区间，除了毒性的目标概率之外，还可以通过指定可接受的毒性概率范围来选择CRM中使用的初始猜测。提出了一种算法，用于获得的无差异区间，最大限度地提高了正确选择的平均百分比，在一组场景的真实概率的毒性，并提供了一个系统的方法，选择初始的猜测，在一个更少的时间消耗的方式比试错法。两个真实的CRM试验的背景下，比较的方法。对于这两项试验，由所提出的算法选择的初始猜测具有相似的操作特征，如通过正确选择的百分比、所选剂量的真实概率与毒性的目标概率之间的平均绝对差与试验进行期间使用的初始猜测相比，各剂量下接受治疗的百分比和毒性的总体百分比，这些猜测是通过试验和错误获得的，耗时的校准过程。在淋巴瘤试验中，试错法与拟议方法相比，所考虑的情景的平均正确选择百分比分别为61.5%和62.0%，在卒中试验中分别为62.9%和64.0%。我们只提供了经验剂量毒性曲线的详细结果，尽管所提出的方法适用于其他剂量毒性模型，如logistic。所提出的方法提供了一种快速和系统的方法，用于选择初始猜测的毒性概率的CRM中使用的竞争力，通过一个耗时的过程中，通过试错获得的那些，从而简化模型校准过程的CRM。
The continual reassessment method (CRM) is an adaptive model-based design used to estimate the maximum tolerated dose in dose finding clinical trials. A way to evaluate the sensitivity of a given CRM model including the functional form of the dose-toxicity curve, the prior distribution on the model parameter, and the initial guesses of toxicity probability at each dose is using indifference intervals. While the indifference interval technique provides a succinct summary of model sensitivity, there are infinitely many possible ways to specify the initial guesses of toxicity probability. In practice, these are generally specified by trial and error through extensive simulations. By using indifference intervals, the initial guesses used in the CRM can be selected by specifying a range of acceptable toxicity probabilities in addition to the target probability of toxicity. An algorithm is proposed for obtaining the indifference interval that maximizes the average percentage of correct selection across a set of scenarios of true probabilities of toxicity and providing a systematic approach for selecting initial guesses in a much less time consuming manner than the trial and error method. The methods are compared in the context of two real CRM trials. For both trials, the initial guesses selected by the proposed algorithm had similar operating characteristics as measured by percentage of correct selection, average absolute difference between the true probability of the dose selected and the target probability of toxicity, percentage treated at each dose and overall percentage of toxicity compared to the initial guesses used during the conduct of the trials which were obtained by trial and error through a time consuming calibration process. The average percentage of correct selection for the scenarios considered were 61.5% and 62.0% in the lymphoma trial, and 62.9% and 64.0% in the stroke trial for the trial and error method versus the proposed approach. We only present detailed results for the empiric dose toxicity curve, although the proposed methods are applicable for other dose toxicity models such as the logistic. The proposed method provides a fast and systematic approach for selecting initial guesses of probabilities of toxicity used in the CRM that are competitive to those obtained by trial and error through a time consuming process, thus, simplifying the model calibration process for the CRM.
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影响因子：
1.9
作者：
OQUIGLEY, J;PEPE, M;FISHER, L
通讯作者：
FISHER, L
影响因子：
2.7
作者：
Cheung, YK
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影响因子：
1.9
作者：
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通讯作者：
Chappell, R
影响因子：
2.7
作者：
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影响因子：
6.7
作者：
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