用贝叶斯联合模型对临床试验中的纵向结果进行调整,以考虑间流事件
Wen Teng1, Yongdong Ouyang2, Jose Dianti3
1Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
Contemporary clinical trials communications
|February 24, 2026
概括
临床试验中的终端间流事件可能会导致结果偏差. 贝叶斯联合建模方法有效地处理这些事件,改善治疗效果估计并增加大约15%的统计能力.
科学领域:
- 生物统计学 生物统计学
- 临床试验方法论 临床试验方法论
- 纵向数据分析 纵向数据分析
背景情况:
- 间歇性事件使临床试验终点的解释和测量复杂化.
- 终端事件可以排除完整的纵向结果评估,导致偏差估计,如果不适当处理.
- 强大的方法对于管理与结果相关的终端互流事件至关重要.
研究的目的:
- 提出和评估贝叶斯联合建模方法来处理临床试验中的终端间流事件.
- 为了提高治疗效果估计在存在不完整的结果数据的准确性和可靠性.
- 为临床试验的设计和分析阶段提供原则性的方法.
主要方法:
- 开发了一个贝叶斯联合模型,同时使用共享的随机效应分析纵向结果和终端事件.
- 采用多个离散时间生存子模型来适应各种事件类型.
- 进行了广泛的模拟,模仿了具有竞争风险 (例如恢复和死亡) 的临床试验.
主要成果:
- 拟议的贝叶斯联合建模方法与忽视间流事件的方法相比,显示出更高的统计能力.
- 在由间流事件带来的大量偏差的场景中,功率增加了大约15%.
- 联合建模有效地减少了由终端间流事件引起的偏差.
结论:
- 贝叶斯联合建模方法在临床试验设计和分析中有效解决终端间流事件.
- 明确考虑与事件相关的纵向随访截止,可以提高治疗效果估计的精度和可靠性.
- 这种方法改善了对临床试验结果的解释,当测量由于终端事件而不完整时.
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