A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2
A Bayesian interval dose-finding design addressingOckham's razor: mTPI-2
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DOI：
10.1016/j.cct.2017.04.006
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发表时间：
2017-07-01
影响因子：
2.2
通讯作者：
Ji, Yuan
中科院分区：
文献类型：
--
作者：
Guo, Wentian;Wang, Sue-Jane;Ji, Yuan
人们对使用基于区间的贝叶斯设计来寻找剂量越来越感兴趣，其中一种是改进的毒性概率区间（mTPI）方法。我们证明了mTPI中的决策规则对应于正式贝叶斯决策理论框架下的最优规则。然而，mTPI中的概率模型被奥卡姆剃刀削得过于锋利，这虽然通常有助于简洁的统计推断，但从安全角度来看，会导致不受欢迎的决策。我们提出了一个新的框架，钝化奥卡姆剃刀，并证明了新方法的优越性能，称为mTPI-2。为用户提供了一个在线网络工具，用户可以生成设计、进行临床试验和检查设计的操作特性。
There has been an increasing interest in using interval-based Bayesian designs for dose finding, one of which is the modified toxicity probability interval (mTPI) method. We show that the decision rules in mTPI correspond to an optimal rule under a formal Bayesian decision theoretic framework. However, the probability models in mTPI are overly sharpened by the Ockham's razor, which, while in general helps with parsimonious statistical inference, leads to undesirable decisions from safety perspective. We propose a new framework that blunts the Ockham's razor, and demonstrate the superior performance of the new method, called mTPI-2. An online web tool is provided for users who can generate the design, conduct clinical trials, and examine operating characteristics of the designs.