遗漏数据的多重归算在随机遗漏下:将碰撞机作为归算模型中的辅助变量可以诱导偏差
Elinor Curnow1,2, Kate Tilling1,2, Jon E Heron1,2
1Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.
Frontiers in epidemiology
|November 17, 2023
概括
多重归算 (MI) 使用辅助变量来处理流行病学研究中缺少的数据. 选择不当的辅助变量,特别是碰撞器,可以引入偏差并增加MI估计的标准误差.
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:
- 在流行病学研究中,缺少数据很普遍.
- 多重归算 (MI) 是处理缺失数据的常用方法.
- 辅助变量可以改善MI,但缺乏明确的选择准则.
研究的目的:
- 检查MI中选择不当的辅助变量的影响.
- 为了确定在归算模型中使用"碰撞器"变量的后果.
- 为了量化偏差和标准误差的变化,由于辅助变量选择.
主要方法:
- 代数推导模型偏差和标准误差.
- 模拟研究用于评估各种失踪情景下的性能.
- 当暴露或结果不完整时,对偏差和标准误差的分析.
主要成果:
- 包括碰撞机辅助变量可以诱导偏差并增加MI的标准误差.
- 当结果被部分观察时,偏差可能是相当大的.
- 当暴露部分观察时,偏差较小,除非结果影响暴露缺失.
结论:
- 在MI中,仔细选择辅助变量至关重要.
- 应评估潜在的辅助变量,以了解它们与结果和缺失机制的关系.
- 了解因果图和缺失机制至关重要,以避免在MI中使用碰撞器.
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