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Abstract：
Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.
摘要：
多变量孟德尔随机化允许同时估计多个暴露变量对结果的直接因果影响。当感兴趣的暴露变量是定量的组学特征时，获得完整的数据可能在经济和技术上都具有挑战性：测量成本很高，并且测量装置可以具有固有的检测极限。在本文中,在单样本多变量孟德尔随机化分析中，我们提出了一种有效且有效的方法来处理暴露变量的未测量和不可检测值。我们使用最大似然估计来估计直接因果效应，并开发了一种期望最大化算法来计算估计器。我们通过模拟研究展示了所提出方法的优势，并为西班牙裔社区健康研究/拉丁美洲人研究提供了应用，其中有大量未测量的暴露数据。
