使用仅预测缺失的辅助归算变量进行多次归算,可能会增加因数据缺失而导致的偏差,而不是随机的数据缺失
Elinor Curnow1,2, Rosie P Cornish3,4, Jon E Heron3,4
1Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. elinor.curnow@bristol.ac.uk.
BMC medical research methodology
|October 7, 2024
概括
在多重归算 (MI) 中包括不相关的辅助变量可以在数据缺失时加剧偏差,而不是随机 (MNAR). 研究人员应该根据他们对缺失数据的预测能力,仔细选择辅助变量,而不仅仅包括所有可用的数据.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 流行病学和临床研究经常遇到缺失的数据,通常使用多重归咎 (MI) 来处理.
- 如果数据不随机 (MNAR) 缺失,多重归算估计可能会有偏见.
- 辅助变量可以减轻MNAR数据的MI偏差,但选择策略需要仔细考虑.
研究的目的:
- 探索包括辅助变量预测缺失的影响,但与MI模型中部分观察到的变量无关.
- 量化线性或逻辑回归模型中由这些辅助变量引入的额外偏差.
- 评估对暴露系数的影响,当部分观察到的变量是结果或暴露时.
主要方法:
- 使用代数量化和模拟研究来评估偏差.
- 这项研究比较了含有和不含特定类型辅助变量的MI模型.
- 分析涉及连续和二进制部分观察变量,应用于结果和暴露.
- 从出生队列研究中重新分析数据说明了这些发现.
主要成果:
- 包括一个预测缺失的辅助变量,但与部分观察到的变量无关,可以引入实质性的额外偏差.
- 这种额外的偏差特别明显,当结果被部分观察时,缺失是由结果本身驱动的.
- 当结果和暴露都对失踪机制有所贡献时,偏见甚至更大.
结论:
- 在数据可能是MNAR时,应避免在MI中包含所有可用的辅助变量的常见做法.
- 辅助变量应根据它们与部分观察到的变量有很强的预测关联来选择.
- 确定适当的辅助变量需要仔细考虑因果图,缺失机制和数据探索,考虑潜在的选择偏差.
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