论文标题
穆斯：用于设计和分析1B多个扩展队列试验的贝叶斯分层建模
MUCE: Bayesian Hierarchical Modeling for the Design and Analysis of Phase 1b Multiple Expansion Cohort Trials
论文作者
论文摘要
我们提出了一种多重队列扩展方法（MUCE）方法，作为1B期膨胀队列试验的设计或分析方法，这是在1A期剂量升级后进行的新的首次人类研究。 Muce的设计基于一类贝叶斯分层模型，这些模型可以适应跨武器的信息。统计推断直接基于每个手臂有效的后验概率，从而促进了决定选择哪个手臂进行进一步测试的决策。
We propose a multiple cohort expansion (MUCE) approach as a design or analysis method for phase 1b multiple expansion cohort trials, which are novel first-in-human studies conducted following phase 1a dose escalation. The MUCE design is based on a class of Bayesian hierarchical models that adaptively borrow information across arms. Statistical inference is directly based on the posterior probability of each arm being efficacious, facilitating the decision making that decides which arm to select for further testing.
