适应性设计与贝叶斯知情临时决策:应用到机械循环支持的随机试验
R Mukherjee1, N Muehlemann2, Y Gao3
1MuSigmas Consultants, Barcelona, Spain.
Therapeutic innovation & regulatory science
|August 16, 2025
概括
本研究引入了使用贝叶斯预测功能的自适应性试验设计,以调整样本大小和随访持续时间以适应时间到事件终点,提高心血管和瘤学试验的效率.
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
- 心血管研究研究心血管研究
- 瘤学研究研究
背景情况:
- 心血管和瘤学试验往往需要大样本大小和延长后续期.
- 对于时间到事件终点的传统方法依赖于比例危险假设,这可能并不总是有效的.
- 适应性试验设计通过重新估计样本大小来提供优化,例如有前途的区域方法.
研究的目的:
- 提出和评估一个使用贝叶斯预测功率 (PP) 的自适应性临床试验设计.
- 根据中间数据指导调整样本大小和/或最低随访时间.
- 提高临床试验的稳定性和效率,以时间到事件的终点.
主要方法:
- 该PROTECT IV试验采用适应性设计,用于高风险的皮肤冠状动脉干预,最初招募了1252名患者,随访期为12个月.
- 临时分析将使用模拟来确定适应性增加样本大小 (高达2500个) 和/或随访 (高达36个月) 以达到至少90%的PP.
- 贝叶斯的零碎常量危险模型适用于临时数据,避免对更可靠的决策进行比例危险假设.
主要成果:
- 模拟检查了设计在延迟治疗效果,早期效益或跨越生存曲线的场景中的实用性.
- 贝叶斯模型,使用后预测分布,促进了强大的临时决策.
- 建议的贝叶斯式方法与频率主义的条件电源相比,展示了更具体的适应规则,具有类似的操作特征.
结论:
- 灵活的建模和利用患者级数据,如计算PP,为适应性试验中的临时决策提供了更强大,更有效的方法.
- 这种方法对具有时间到事件终点的试验特别有益,在这些试验中预计生存曲线的交叉.
- 拟议的贝叶斯适应设计提高了与依赖比例危险假设的传统方法相比,对样本大小调整的决策.
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