基于优化的稳定平衡权重与高共变异失衡样本的倾向性得分权重对比
Stuart R Wallace1, Sachinkumar B Singh1, Rebekah Blakney1
1MedTech Epidemiology and Real-World Data Sciences, Johnson & Johnson, New Brunswick, New Jersey, USA.
Pharmacoepidemiology and drug safety
|July 16, 2024
概括
与倾向分数权重 (PSW) 相比,稳定平衡权重 (SBW) 改善了共变量平衡和有效样本大小 (ESS). SBW在预规定的共同变量平衡目标方面提供了灵活性,在应用医疗保健数据库分析方面表现优于PSW.
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:
- 倾向性得分权重 (PSW) 是观察性研究中共变量平衡的常用方法.
- 稳定平衡权重 (SBW) 提供了实现共变量平衡的替代方法.
- 将SBW和PSW的性能进行比较对于选择最佳权重方法至关重要.
研究的目的:
- 为了比较稳定平衡权重 (SBW) 与倾向分数权重 (PSW) 的性能.
- 使用这两种方法来评估共变量平衡和有效样本大小 (ESS).
- 在极端共变异失衡和实质性样本大小差异的情况下评估性能.
主要方法:
- 在两个涉及外科手术和神经系统手术的应用案例中使用了Premier Healthcare数据库.
- 对所处理的 (ATT) 权重产生的平均治疗效应.
- 使用网格搜索和预规定的SMD容忍技术实施SBW.
- 在加权后比较SBW和PSW标准化平均差异 (SMD),不平衡共变量数和ESS.
主要成果:
- 这两种SBW技术都改善了共变量平衡.
- 与PSW相比,SBW方法在对照组中获得了更高的ESS.
- 使用SBW和变量特定SMD值的灵敏度分析进一步提高了ESS,表现优于PSW.
- 所有方法都导致后加权ESS低于原来的未加权样本大小.
结论:
- 基于优化的SBW在预先指定共变量平衡目标方面提供了灵活性.
- 在应用分析中,SBW导致高于加权后的共变量平衡和较大的ESS,而不是PSW.
- SBW是PSW的一个有希望的替代品,可以从观测数据中增强因果推断.
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