从观察性研究中推断延伸的方法:考虑因果结构,识别假设和估计器
Eleanor Hayes-Larson1, Yixuan Zhou1,2, L Paloma Rojas-Saunero1
1From the Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA.
Epidemiology (Cambridge, Mass.)
|August 9, 2024
概括
这项研究旨在扩展观察性研究的发现,这对于生命过程流行病学至关重要. 它详细介绍了确保因果效应估计能够对目标人群进行概括的方法,即使具有复杂的选择偏差.
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:
- 定量概括性和可转移性通常集中在随机试验上.
- 从观察性研究中扩展发现,由于复杂的选择机制,提出了独特的挑战.
研究的目的:
- 描述可识别性假设和用于将因果效应估计从观察性研究扩展到目标人群的方法.
- 要突出方法的差异,与扩展随机试验的发现相比.
主要方法:
- 描述了可识别性假设和使用观察到的数据进行识别.
- 采用统计方法,包括权重,结果建模和双重可靠的方法.
- 通过模拟研究来说明方法的性能.
主要成果:
- 估计者必须解决混,当选择发生在暴露和混因素时.
- 当选择还涉及调解者时,估计者需要使用不同的变量集来解释选择和混的方法.
- 在模拟中展示了各种统计方法的性能.
结论:
- 提出的方法适用于观察性研究中常见的复杂因果结构,特别是生命周期流行病学.
- 确定了在现实世界中应用这些方法的概念意义和实际问题.
- 强调需要强大的方法来处理选择偏差和混,以获得有效的因果推理.
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