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首页> 外文期刊>Current Research in Biostatistics. >A Bayesian Adaptive Design for Combination of Three Drugs in Cancer Phase I Clinical Trials
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A Bayesian Adaptive Design for Combination of Three Drugs in Cancer Phase I Clinical Trials

机译：结合贝叶斯自适应设计三种药物在癌症第一阶段临床试验

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摘要

We describe a Bayesian adaptive design for early phase cancer trials of a combination of three agents. This is an extension of an earlier work by the authors by allowing all three agents to vary during the trial and by assigning different drug combinations to cohorts of three patients. The primary objective is to estimate the Maximum Tolerated Dose (MTD) surface in the three-dimensional Cartesian space. A class of linear models on the logit of the probability of Dose Limiting Toxicity (DLT) are used to describe the relationship between doses of the three drugs and the probability of DLT. Trial design proceeds using conditional escalation with overdose control, where at each stage of the trial, we seek a dose of one agent using the current posterior distribution of the MTD of this agent given the current doses of the other two agents. The MTD surface is estimated at the end of the trial as a function of Bayes estimates of the model parameters. Operating characteristics are evaluated with respect to trial safety and percent of dose recommendation at dose combination neighborhoods around the true MTD surface.
机译：我们描述一个贝叶斯自适应设计癌症阶段试验的三个代理。作者通过允许所有三个特工在试验和通过分配不同的变化药物组合群三个病人。的主要目的是估计的最大耐受剂量(MTD)的表面三维笛卡尔坐标系。的分对数线性模型的概率剂量限制毒性(DLT)是用于描述三种药物的剂量之间的关系和DLT的概率。使用条件与过量升级控制,在每个阶段的试验,我们寻找一个代理使用当前的剂量这个代理的MTD的后验分布考虑到当前的剂量的其他两个代理。MTD表面估计的结束审判作为贝叶斯估计的函数模型参数。评估对安全性和审判剂量率推荐剂量在真正的MTD结合社区表面。

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