在篮子试验中的一般化三重结果决策
Journal of biopharmaceutical statistics
|January 3, 2024
概括
我们引入了通用三重结果决策 (G-TODeM),以加强临床试验的去/不去决策. 这种方法将贝叶斯方法与跨队列的信息借用相结合,以实现更灵活的试验管理.
科学领域:
- 临床试验 临床试验
- 生物统计学 生物统计学
- 药物开发 药物开发
背景情况:
- 在II期/Ib期临床试验中做/不做决定对药物开发至关重要.
- 为了更大的灵活性,传统的二进制假设测试正在被三重结果决策 (TODeM) 取代.
- 现有的贝叶斯信息借贷方法仅限于二元决策框架.
研究的目的:
- 提出一种新的决策框架,即通用三重结果决策 (G-TODeM).
- 为TODeM框架适应贝叶斯方法与信息借用.
- 通过结合跨队列数据,提高临床试验决策灵活性.
主要方法:
- 发展通用三重结果决策 (G-TODeM) 框架.
- 应用贝叶斯方法,如EXNEX和MUCE,用于跨队列的信息借用.
- 在TODeM结构中整合这些贝叶斯式方法.
主要成果:
- G-TODeM框架成功地将贝叶斯式信息借用应用到TODeM结构中.
- 从其他队列中借取信息有效地缩小了推断队列的"考虑"区域.
- 这导致了更细微和数据驱动的去/不去决策.
结论:
- G-TODeM提供了一种可靠的方法来增强临床试验决策.
- 该框架通过允许贝叶斯统计和TODeM的灵活整合来改进现有方法.
- 这种方法有助于更有效,更有信息的临床试验进展.
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