使用定向非循环图来确定暴露结果关联的多重归算或亚样本-多重归算估计是否是无偏的
Paul Madley-Dowd1,2,3,4, Rachael A Hughes1,2, Maya B Mathur5
1MRC Integrative Epidemiology Unit at the University of Bristol, United Kingdom.
American journal of epidemiology
|November 25, 2025
概括
多重归算 (MI) 可以在缺少随机 (MAR) 数据的情况下有效,但对多个不完整变量缺乏实际指导. 本研究介绍了一种定向非循环图算法,以确定MI何时对暴露结果系数无偏差.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 统计建模 统计建模
背景情况:
- 缺少数据是流行病学研究中常见的挑战.
- 多重归算 (MI) 是一种广泛用于处理缺失数据的方法.
- 在随机缺失 (MAR) 假设下,评估具有多个不完整变量的MI的有效性是复杂的,缺乏实际指导.
研究的目的:
- 开发使用定向非循环图 (DAG) 的算法,以确定MI提供无偏见的暴露结果系数估计的条件.
- 扩展基于DAG的算法,以评估MI在部分样本中的有效性,其中一些变量是完整的,而另一些则是归算的.
- 为研究人员提供实用工具,以便在处理多个不完整变量时决定适当使用MI.
主要方法:
- 使用定向非循环图 (DAG) 来表示因果关系和缺失数据机制.
- 开发了一种基于DAG的新算法,以评估无偏 MI 的条件.
- 将算法应用于理论示例和现实世界流行病学数据集.
主要成果:
- 基于DAG的算法可以识别MI产生无偏的暴露结果系数的场景.
- 扩展算法可以确定子样本MI的有效性,当一些变量是完整的,而另一些则是归算的.
- 对现实实例的分析表明,只有对结果变量的子样本归算才能产生有效的结果.
结论:
- 拟议的算法为应用研究人员提供了一种系统的方法,以评估多个不完整变量的多重归算的有效性.
- 这些发现强调了在应用MI时仔细考虑缺失数据模式和可变完整性的重要性.
- 需要进一步的研究来量化潜在的偏差和各种缺失数据模式对MI有效性的影响.
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