使用参数工作模型进行双强度差异估计
Bonnie E Shook-Sa1, Paul N Zivich2, Chanhwa Lee1
1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States.
Biometrics
|May 5, 2025
概括
两倍强大的差异估计器提供可靠的因果推理. 与传统的影响函数不同,实证的三明治和引导方法在结果或暴露模型正确时提供有效的方差估计.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 因果推理因果推理
背景情况:
- 双强度 (DR) 估计器在因果推断中很受欢迎,当结果或暴露模型被正确指定时,可以进行一致的估计.
- 对于DR估计器的传统基于影响函数的方差估计器缺乏稳定性,需要对两个模型进行正确的规范以保持一致性.
- 这一限制对非随机的风险特别重要,因为模型的错误规范是常见的.
研究的目的:
- 评估实证三明治和非参数引导差异估计器的双重稳定性.
- 将这些估计器的性能与传统的基于影响功能的估计器进行比较.
- 在模型错误规范下评估差异估计和置信区间覆盖的有效性.
主要方法:
- 该研究理论上表明,实证三明治和非参数引导差异估计器的稳定性是两倍的.
- 模拟研究以假设参数工作模型进行,以比较不同差异估计器的性能.
- 估计者被应用到从改善孕期结果与孕激素 (IPOP) 研究的现实世界数据.
主要成果:
- 实证三明治和非参数引导差异估计器表现出两倍强大的性能.
- 当至少有一个工作模型 (结果或暴露) 正确指定时,这些方法可提供有效的差异估计和名义置信区间覆盖.
- 模拟结果证实了理论发现,强调了这些替代差异估计器的稳定性.
结论:
- 与传统的基于影响函数的方法相比,实证的三明治和非参数引导差异估计器在因果推理中提供了更可靠的差异估计,特别是在非随机暴露的情况下.
- 这些强大的方法提高了可信度区间的有效性,当工作模型可能被错误指定时.
- 这些发现支持使用这些双重可靠的差异估计器,以在观察性研究中改进因果效应估计.
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