在倾向性得分分析之前对缺失的共变量数据的归算:实践方法的强度的教程和评估
Walter L Leite1, Burak Aydin2, Dee D Cetin-Berber3
1University of Florida, Gainesville, FL, USA.
Evaluation review
|August 1, 2025
概括
多重归算 (MI) 和单一归算 (SI) 方法有效处理缺少的共变量数据用于倾向得分分析 (PSA). 跨MI的表现很好,而SI则足以满足最小的缺失数据,确保在准实验性研究中强有力的偏差减少.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 倾向性评分分析 (PSA) 对于减轻准实验研究中的选择偏差至关重要.
- 在PSA之前处理缺少的共变量数据对于准确的倾向性得分估计至关重要.
- 多次归算 (MI) 和单次归算 (SI) 是解决PSA缺失数据的常见方法.
研究的目的:
- 在PSA之前审查MI-within,MI-across和SI方法来处理缺少的共同变量数据.
- 用蒙特卡洛模拟来评估MI-across和SI方法的稳定性.
- 用一个实际的,逐步的例子来说明缺失的数据处理和PSA.
主要方法:
- 蒙特卡洛模拟对连续和分类共变量的MI-across和SI归算策略进行了比较.
- 在模拟条件下,样本大小,共变量数,治疗效果大小,缺失数据机制和缺失数据的百分比各不相同.
- 推算技术包括通过联合建模或通过链式方程 (MICE) 进行多变量推算的MI-across和SI.
主要成果:
- MI-across方法在倾向性得分估计方面表现强.
- 单次归算 (SI) 进行得足够好,特别是缺失数据的百分比较小.
- 一个说明性的例子成功地展示了MI和SI,倾向性得分权重,共变量平衡和治疗效果估计.
结论:
- MI-across是一种强大的方法,用于处理PSA中缺少的共变量数据.
- 当缺失数据百分比较低时,SI可以是一个可行的替代方案.
- 该研究为在现实研究中实施这些方法提供了实用指南.
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