Guo Wentian, Wang Sue-Jane, Yang Shengjie, Lynn Henry, Ji Yuan
School of Public Health, Fudan University, PR China.
Office of Biostatistics/Office of Translational Sciences, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, United States.
Contemp Clin Trials. 2017 Jul;58:23-33. doi: 10.1016/j.cct.2017.04.006. Epub 2017 Apr 27.
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.
人们对使用基于区间的贝叶斯设计进行剂量探索的兴趣与日俱增，其中一种方法是改良毒性概率区间（mTPI）法。我们表明，mTPI中的决策规则对应于形式贝叶斯决策理论框架下的最优规则。然而，mTPI中的概率模型因奥卡姆剃刀原则而过度简化，虽然这通常有助于进行简洁的统计推断，但从安全性角度来看会导致不良决策。我们提出了一个弱化奥卡姆剃刀原则的新框架，并展示了名为mTPI-2的新方法的卓越性能。为用户提供了一个在线网络工具，用户可以生成设计、进行临床试验并检查设计的操作特征。
