方法比较和估计失眠对健康结果的因果作用在调查样本人口的调查
Anja Shahu1,2, Joon Chung2, Wassim Tarraf3
1Department of Biostatistics, Harvard T.H. Chan of Public Health, Boston, MA, USA.
Scientific reports
|June 17, 2023
概括
从调查数据中推断因果关系需要仔细处理调查重量和设计. 结合调查重量作为共变量的匹配方法提高了准确性,特别是与未测量的混因子,揭示失眠.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 调查方法 调查方法
背景情况:
- 从调查数据中推断因果关系需要整合调查权重和设计,以进行代表性效应估计和准确的标准误差 (SE).
- 存在各种权重和匹配方法用于因果推理,但它们与复杂的调查数据的性能需要仔细评估.
研究的目的:
- 为了比较不同的方法,将调查权重和设计纳入权重和基于匹配的因果推理方法.
- 在处理可能与调查样本设计相关的未测量混因素时,确定最强大的方法.
主要方法:
- 进行了一项模拟研究,以评估各种因果推理方法在模型规范和未测量的混的不同场景下的性能.
- 进行比较的方法包括权重和匹配方法,调查重量和设计特征的结合方式有所不同.
- 该研究还将选定的方法应用于西班牙裔社区健康研究/拉丁裔研究 (HCHS/SOL) 数据集.
主要成果:
- 当模型被正确指定时,大多数因果推理方法的执行都充足.
- 然而,在与调查权重相关的未测量混因子存在的情况下,只有使用调查权重在因果估计和作为匹配共变量时的匹配方法保持了良好的表现.
- 对HCHS/SOL的申请表明,失眠与轻度认知障碍 (MCI) 和事件高血压之间存在因果关系.
结论:
- 为了从调查数据中推断因果关系,特别是当未测量的混因素可能与样本设计相关时,建议将调查重量作为匹配程序中的共变量,除了用于因果关系估计外.
- 失眠与美国西班牙裔/拉丁裔人口中轻度认知障碍和事件高血压风险增加有关.
- 在复杂的调查数据中,强大的方法对于有效的因果推断至关重要.
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