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用目标样本总结信息构建因果概括估计的置信区间

Yi Chen¹, Guanhua Chen¹, Menggang Yu²

¹Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, USA.

Statistics in medicine

|January 22, 2026

概括

这项研究引入了一种新方法,用于估计目标人群的平均治疗效果 (ATE),即使有协变量转移. 该方法使用总结数据来建立置信区间,改善生物医学研究中的因果推理.

科学领域:

生物统计学生物统计学
流行病学流行病学
因果推理因果推理

背景情况:

在生物医学研究中,将诸如平均治疗效应 (ATE) 这样的因果发现泛化为群体至关重要.
共变量转移,或治疗效果修饰剂分布的差异,可以导致ATEs在源和目标人群之间发生变化.
使用汇总数据估计目标ATEs的现有权重方法在统计推断方面面临局限性,因为它依赖于个体级数据来计算差异.

研究的目的:

提出一种基于重新抽样的新型扰动方法,用于构建估计目标ATE的置信区间.
在只有摘要级信息可用的情况下,为目标ATE提供准确的统计推断.
为了解决先前方法的局限性,这些方法需要个人级别的数据来进行差异估计.

主要方法:

开发了一种基于重新采样的扰动技术,用于置信区间的构建.
从目标样本中利用了额外的摘要级信息.
在模拟研究和现实数据分析中应用该方法.

主要成果:

提出的方法有效地构建了目标ATE的置信区间,仅使用总结级数据.
在模拟环境中证明了该方法的有效性和有效性.
验证了该方法在现实数据集上的性能,证实了其实际适用性.

结论:

基于重新抽样的扰动方法为在共变量转移下对目标ATE的统计推断提供了可行的解决方案,当仅可访问总结数据时.
这种方法克服了先前方法的局限性,避免了需要个人级别的目标数据.
这些发现增强了利用易于获得的总结统计数据在生物医学研究中概括因果关系发现的能力.

关键词:

因果概括的原因概括.在信任间隔的信任间隔.平衡权重的平衡权重.基于重新采样的扰动.总结层次的数据是总结层次的数据.

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