在队列研究中解决未测量的混因素:对结果轨迹上的固定时间暴露的仪器变量方法
Kateline Le Bourdonnec1, Cécilia Samieri1, Christophe Tzourio1
1Inserm, BPH, U1219, University of Bordeaux, Bordeaux, France.
Biometrical journal. Biometrische Zeitschrift
|December 15, 2023
概括
仪表变量方法解决了观察性研究中未测量的混问题. 这项研究将这些方法适应纵向结果,证明它们在分析2型糖尿病和认知衰退方面的有用性.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 遗传学 是一个遗传学.
背景情况:
- 在观察性研究中,未测量的混是一个重大挑战,可能会影响暴露与结果的关联.
- 估计基线暴露对纵向结果轨迹的因果影响需要强大的方法来处理混.
- 仪表变量 (IV) 方法通过利用外源变量来隔离暴露的因果作用提供了一个潜在的解决方案.
研究的目的:
- 适应并以教学方式解释仪器变量方法的应用,以估计基线暴露对重复结果轨迹的无证效应.
- 用模拟研究来说明IV方法在处理未测量的混时的实用性.
- 应用拟议的IV方法来研究2型糖尿病与大队列认知轨迹之间的关联.
主要方法:
- 对纵向数据的两阶段经典仪器变量方法的调整.
- 第一个阶段:使用仪器变量 (42种遗传多态) 预测暴露.
- 第二阶段:将预测的暴露纳入混合效应模型,以估计其与结果轨迹的关联,以及差异估计.
主要成果:
- 模拟研究表明,在未测量的混杂存在的情况下,经典分析的局限性以及IV方法的有效性.
- 对3C队列的应用 (n=6224) 提供了估计2型糖尿病和随后的认知轨迹之间的关联,使用遗传多态度作为IVs.
- 提供了该方法的R实现,以促进其在其他研究环境中的应用.
结论:
- 经过调整的仪器变量方法有效地解决了在重复结果测量的背景下内源性的问题.
- 这项研究为对纵向数据的因果推理感兴趣的研究人员提供了一个实际的框架和工具 (R实现).
- 建议谨慎使用,因为依赖于仪器变量假设,这些假设很难经验测试.
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