缺少数据的多重归算在随机缺失下:如果它们被错误指定,兼容的归算模型不足以避免偏差
Elinor Curnow1, James R Carpenter2, Jon E Heron1
1Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, University of Bristol, Bristol, UK.
Journal of clinical epidemiology
|June 21, 2023
概括
标准的多重归算 (MI) 可以在流行病学研究中引入因默认的线性协变函数而导致的偏差. 研究人员可以使用提出的方法识别和纠正有问题的归算模型,以确保准确的结果.
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:
- 缺少的数据在流行病学研究中很常见.
- 多重归算 (MI) 是处理缺失数据的标准方法.
- 默认MI程序经常使用简单的线性协变函数.
研究的目的:
- 检查由默认MI程序引起的偏差.
- 评估识别有问题的归算模型的方法.
- 为研究人员提供实际指导.
主要方法:
- 使用模拟和真实数据分析.
- 研究了归算模型错误规范对MI性能的影响.
- 将MI与完整记录分析 (CRA) 进行比较.
主要成果:
- 错误指定结果,暴露或混因素之间的关系可能会导致CRA和MI估计偏差.
- 通过预测平均值匹配的MI可以减轻模型错误规范.
- 检查模型错误规范的方法有效地确定了有问题的关系.
结论:
- 分析和归算模型之间的兼容性是必要的,但不足以避免MAR数据中的偏差.
- 提出了一步一步的程序来识别和纠正归算模型的错误规范.
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