Calibration of prior variance in the Bayesian continual reassessment method.
Calibration of prior variance in the Bayesian continual reassessment method.
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DOI：
10.1002/sim.4139
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发表时间：
2011-07-30
影响因子：
2
通讯作者：
Cheung, Ying Kuen
中科院分区：
文献类型：
--
作者：
Lee, Shing M.;Cheung, Ying Kuen
连续再评估方法（CRM）是一种基于模型的自适应设计，用于估计I期临床试验中的最大耐受剂量。渐近地，该方法已被证明是选择正确的剂量给定的某些条件得到满足。当样本量较小时，指定合理的模型是重要的。虽然已经提出了一种算法，用于校准的毒性概率的初始猜测，校准的先验分布的参数贝叶斯CRM还没有得到解决。本文对正态先验分布引入了最小信息先验方差的概念。我们还提出了两个系统的方法来联合校准的先验方差和初始猜测的概率毒性在每个剂量。所提出的校准方法进行了比较，与现有的方法的上下文中的两个例子，通过模拟。新的方法和以前提出的方法产生非常相似的结果，因为后者使用适当的模糊先验。然而，新的方法产生一个较小的间隔毒性概率，其中相邻的剂量可以选择。
The continual reassessment method (CRM) is an adaptive model-based design used to estimate the maximum tolerated dose in phase I clinical trials. Asymptotically, the method has been shown to select the correct dose given that certain conditions are satisfied. When sample size is small, specifying a reasonable model is important. While an algorithm has been proposed for the calibration of the initial guesses of the probabilities of toxicity, the calibration of the prior distribution of the parameter for the Bayesian CRM has not been addressed. In this paper, we introduce the concept of least informative prior variance for a normal prior distribution. We also propose two systematic approaches to jointly calibrate the prior variance and the initial guesses of the probability of toxicity at each dose. The proposed calibration approaches are compared with existing approaches in the context of two examples via simulations. The new approaches and the previously proposed methods yield very similar results since the latter used appropriate vague priors. However, the new approaches yield a smaller interval of toxicity probabilities in which a neighboring dose may be selected.
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