一个贝叶斯平台试验设计与混合控制基于多源可交换性建模的混合控制
Wei Wei1, Ondrej Blaha1, Denise Esserman1
1Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA.
Statistics in medicine
|April 10, 2024
概括
本研究介绍了使用多源可交换性建模 (MEM) 的贝叶斯平台试验设计,以有效地使用历史数据进行罕见疾病试验. 这种方法通过借鉴过去研究的信息来减少标准护理 (SOC) 部门的样本大小.
科学领域:
- 临床试验方法论 临床试验方法论
- 生物统计学 生物统计学
- 罕见疾病研究 罕见疾病研究
背景情况:
- 在临床试验中,特别是在罕见疾病中,将患者纳入标准护理 (SOC) 部门面临重大挑战,包括有限的资源,患者可用性和道德问题.
- 历史试验往往提供了SOC治疗效果的记录良好的证据,提供了利用这些数据的机会.
研究的目的:
- 提出一个新的贝叶斯平台试验设计,其中包含一个利用历史控制数据的混合控制臂.
- 采用多源可交换性建模 (MEM) 框架进行高效的计算和数据源可交换性的正式评估.
主要方法:
- 拟议的设计包括一个贝叶斯平台试验与混合控制臂.
- 多源交换性建模 (MEM) 用于评估历史数据和并发数据的兼容性,以告知数据借用决策.
- 进行了广泛的模拟研究,以评估拟议的混合动力设计的性能.
主要成果:
- 拟议的基于MEM的混合设计显示,内部控制臂的样本大小显著减少.
- 与其他贝叶斯方法相比,当历史数据和内部数据兼容时,设计有效地借用了更多的信息.
- MEM提供了一种计算效率高的方法,用于评估不同数据源之间的可交换性.
结论:
- 使用MEM提出的贝叶斯平台试验设计通过利用历史数据,为罕见疾病临床试验提供了一种高效和有效的策略.
- 这种方法可以大幅减少样本规模,并改善内部控制部门的数据利用率.
- MEM框架促进了知情的数据借用决策,提高了试验结果的可靠性.
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