在临床试验参与效应的情况下,从随机试验中推广和运输因果推理
Lawson Ung1,2, Tyler J VanderWeele1,2,3, Issa J Dahabreh1,2,3,4
1From the CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA.
Epidemiology (Cambridge, Mass.)
|April 23, 2025
概括
这项研究引入了新的方法,将试验结果推广到现实世界的人口中,即使试验参与本身也会影响结果. 它允许在实验环境之外准确估计治疗效果.
科学领域:
- 因果推理的原因推理.
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 将随机试验结果推广到目标人群通常假设试验参与效应不存在.
- 试验参与效应,即参与影响治疗独立的结果,使因果推理复杂化.
- 以前扩展试验结果的方法隐式忽视了这些参与效应.
研究的目的:
- 在临床试验参与效应的存在下,为概括性和可转移性分析定义新的因果估计.
- 根据特定假设提出这些分析的识别结果.
- 为现有的概括性和可运输性估计器提供新的解释.
主要方法:
- 定义了新的概括性和可转移性的因果估计.
- 在假设试验参与和治疗分配之间没有因果相互作用的情况下,开发了识别结果.
- 结合试验数据与目标人群共变量数据.
主要成果:
- 在目标人群的通常护理环境中成功确定了平均治疗效应,即使有试验参与效应.
- 衍生出的识别函数与在更强的假设下先前工作中的函数相匹配.
- 确定试验参与效应可以在没有相互作用的情况下考虑.
结论:
- 拟议的方法允许在存在试验参与效应的情况下对试验结果进行有效的概括和可转移性.
- 为现有的估计器提供了新的解释,扩大了它们的适用性.
- 在怀疑试验参与效应但相互作用微不足道的场景中有用.
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