结果(博弈论)
贝叶斯概率
灵活性(工程)
统计推断
相关性(法律)
贝叶斯推理
计算机科学
临床决策
推论
计量经济学
机器学习
统计
人工智能
数学
医学
数理经济学
重症监护医学
法学
政治学
标识
DOI:10.1080/10543406.2023.2296054
摘要
Making the go/no-go decision is critical in Phase II (or Ib) clinical trials. The conventional decision-making framework based on a binary hypothesis testing has been gradually replaced by the TODeM (Triple Outcome Decision-Making) which has three zones of outcomes: go, no-go, and consider. The TODeM provides more flexibility in decision-making with considering both of statistical significance and clinical relevance. However, Bayesian methods (e.g. EXNEX, MUCE, etc.) for the information borrowing are still based on the binary decision-making framework. We propose a new decision-making process G-TODeM (Generalized Triple Outcome Decision-Making) to apply those Bayesian methods with information borrowing across different cohorts to the TODeM framework. Essentially, the information borrowed from other cohorts can shrink the consider zone of the inference cohort.
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