缺少数据的多重归算与跳过模式共变量:替代策略的比较
Guangyu Zhang1, Yulei He1, Bill Cai1
1National Center for Health Statistics, Hyattsville, MD, US.
概括
对调查数据的多重归算 (MI) 方法与缺失值和跳过模式进行比较. 仅对适用情况 (IAAC) 进行缺失数据的归算,并对所有情况下进行重新编码 (IWRNC) 的归算进行了评估.
科学领域:
- 统计 统计 统计 统计
- 调查方法 调查方法
- 生物统计学 生物统计学
背景情况:
- 多重归算 (MI) 是处理调查中缺少数据的标准技术.
- 跳过模式,当问题不适用于某些参与者时,会使标准MI的实施复杂化.
- 现有的MI方法可能无法直接解决跳过模式的共变量中缺失的值.
研究的目的:
- 为了比较两个不同的MI方法来处理跳过模式共变量的缺失值.
- 评估适用案例 (IAAC) 之间的归算与重新编码的非适用案例 (IWRNC) 的归算的性能.
- 为复杂的调查数据选择适当的MI策略提供指导.
主要方法:
- 开发了两个MI方法:IAAC和IWRNC.
- IAAC仅在适用主题的子集内赋值缺失值.
- IWRNC将所有主题的缺失值归因为缺失值,并使用非适用情况的重新编码策略.
- 设计了一项模拟研究,以评估这两种方法的性能.
- 这些方法应用于来自国家卫生统计中心 (2015-2016年研发调查) 的现实调查数据.
主要成果:
- 模拟结果显示IAAC和IWRNC在各种缺失数据场景下的性能差异.
- 该研究确定了IAAC表现优于IWRNC的特定情况,反之亦然.
- 对研发调查数据的应用表明了在两种MI方法之间进行选择的实际含义.
结论:
- 在IAAC和IWRNC之间做出选择取决于跳过模式和缺失数据的特定特征.
- 仔细考虑MI实施对于准确分析跳过模式的调查数据至关重要.
- 无论是IAAC还是IWRNC都提供了可行的解决方案,但它们的有效性各不相同,需要根据数据结构选择方法.
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