使用选择图解去神秘化不一致的两样本孟德尔随机化估计
Lei Hou1,2, Yuanyuan Yu1,2, Zhi Geng3
1Department of Medical Data, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250000, People's Republic of China.
BMC medical research methodology
|December 13, 2025
概括
两个样本的门德尔随机化 (TSMR) 可能会受到不同的人口分布的偏差,称为不同的局部机制. 本研究定义了不一致的TSMR估计 (InTSMRE) 并引入LATE比率来量化因果效应偏差.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 遗传流行病学遗传流行病学
背景情况:
- 双样本门德尔随机化 (TSMR) 是一种强大的因果推理方法,但容易受到来自人口特异变量分布的偏差的影响.
- 暴露,结果或混物分布在人群中的差异,称为不同的局部机制,可能会损害TSMR的有效性.
- 不测量的混是TSMR所解决的一个关键挑战,但局部机制差异引入了一个独特的偏见来源.
研究的目的:
- 澄清不同局部机制对TSMR局部平均治疗效应 (LATE) 估计的影响.
- 正式定义完整和部分不一致的TSMR估计 (InTSMRE).
- 引入LATE比率来量化LATE估计与真实因果关系差异的偏差.
主要方法:
- 利用选择图来分析不同局部机制对TSMR的影响.
- 开发了适用于连续结果和二进制结果的"没有InTSMRE"标准.
- 提出LATE比率作为衡量TSMR因果效应估计准确性的指标.
- 进行模拟研究以确定导致InTSMRE的条件.
主要成果:
- 定义和区分基于本地机制的完整和部分InTSMRE.
- 已经证明,违反单调性条件会导致完整的InTSMRE,而违反单调性条件会导致部分的InTSMRE.
- 介绍了LATE比率,显示其在评估InTSMRE的大小中的实用性.
- 在一项研究中实证地证明了InTSMRE的腰比和2型糖尿病在欧洲和混合人群中.
结论:
- 不同的局部机制对TSMR因果推理的有效性构成重大挑战.
- 拟议的框架和LATE比率提供了工具,以识别和量化TSMR因人口分布不同而导致的偏差.
- 了解和解决InTSMRE对于在遗传流行病学中可靠的因果效应估计至关重要.
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