使用选择仪器来调整孟德尔随机化中的选择偏差
Apostolos Gkatzionis1,2, Eric J Tchetgen Tchetgen3, Jon Heron1,2
1MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom.
Statistics in medicine
|July 22, 2024
概括
流行病学研究中的选择偏差可以使用Heckman来解决.
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:
- 选择偏见是流行病学研究的一个重大挑战,通常被概念化为缺失数据问题.
- 像反向概率权衡这样的标准方法假定数据是随机丢失的,如果违反,可能导致偏差的结果.
- 赫克曼的样本选择模型提供了一种方法来调整缺失的结果数据,而不是随机缺失的结果数据.
研究的目的:
- 审查Heckman的样本选择模型和Tchetgen Tchetgen和Wirth (2017) 的相关方法.
- 为了证明这些方法在孟德尔随机化 (MR) 分析中的应用,缺乏暴露或结果数据.
- 评估与参与研究相关的遗传变异作为选择工具变量的实用性.
主要方法:
- 对Heckman的样本选择模型和Tchetgen Tchetgen和Wirth (2017) 方法的审查.
- 在缺少个体级数据的情况下对门德尔随机化 (MR) 分析的应用.
- 缺失调整估计方法的描述:沃尔德比率,两阶段最小平方和反差加权.
主要成果:
- 赫克曼的方法和Tchetgen和Wirth (2017) 的方法都能减轻MR分析中的选择偏差.
- 在某些场景中,这些方法可能会产生具有实质性标准误差的参数估计.
- 一个应用程序研究了身体质量指数对吸烟的影响,使用了Avon长度研究父母和孩子的数据.
结论:
- 赫克曼的样本选择模型和相关方法为解决缺少数据的门德尔随机化研究中的选择偏差提供了有价值的工具.
- 在应用这些技术时,需要仔细考虑潜在的标准错误.
- 这些方法适用于现实世界的流行病学数据,如身体质量指数和吸烟示例所示.
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