Adaptive prior variance calibration in the Bayesian continual reassessment method.
Adaptive prior variance calibration in the Bayesian continual reassessment method.
复制标题
贝叶斯连续重评估方法中的自适应先验方差校准。
DOI：
10.1002/sim.5621
复制
发表时间：
2013
影响因子：
2
通讯作者：
Taylor,JeremyMG
中科院分区：
文献类型：
--
作者：
Zhang,Jin;Braun,ThomasM;Taylor,JeremyMG
持续再评估方法（CRM）和其他基于模型的方法在设计I期临床试验中的使用有所增加，因为CRM比3 + 3方法更能确定最大耐受剂量。然而，CRM可能对为模型参数的先验分布选择的方差敏感，特别是当招募少量患者时。虽然已经出现了自适应地选择骨架和校准的先验方差只在一个试验的开始，还没有任何方法开发的自适应校准的先验方差在整个试验。我们提出了三个系统的方法来自适应校准的先验方差在试验过程中，并通过模拟提出的方法来校准方差在试验开始时进行比较。版权所有© 2012约翰威利父子有限公司.
The use of the continual reassessment method (CRM) and other model‐based approaches to design Phase I clinical trials has increased owing to the ability of the CRM to identify the maximum tolerated dose better than the 3 + 3 method. However, the CRM can be sensitive to the variance selected for the prior distribution of the model parameter, especially when a small number of patients are enrolled. Although methods have emerged to adaptively select skeletons and to calibrate the prior variance only at the beginning of a trial, there has not been any approach developed to adaptively calibrate the prior variance throughout a trial. We propose three systematic approaches to adaptively calibrate the prior variance during a trial and compare them via simulation with methods proposed to calibrate the variance at the beginning of a trial. Copyright © 2012 John Wiley & Sons, Ltd.