Exploratory Mediation Analysis with Many Potential Mediators

Social and behavioral scientists are increasingly employing technologies such as fMRI, smartphones, and gene sequencing, which yield ‘high-dimensional’ datasets with more columns than rows. There is increasing interest, but little substantive theory, in the role the variables in these data play in known processes. This necessitates exploratory mediation analysis, for which structural equation modeling is the benchmark method. However, this method cannot perform mediation analysis with more variables than observations. One option is to run a series of univariate mediation models, which incorrectly assumes independence of the mediators. Another option is regularization, but the available implementations may lead to high false-positive rates. In this article, we develop a hybrid approach which uses components of both filter and regularization: the ‘Coordinate-wise Mediation Filter’. It performs filtering conditional on the other selected mediators. We show through simulation that it improves performance over existing methods. Finally, we provide an empirical example, showing how our method may be used for epigenetic research.

社会与行为科学研究者日益广泛采用功能磁共振成像（fMRI）、智能手机、基因测序等技术，这类技术可生成列数多于行数的“高维”数据集。学界对这些数据中的变量在已知研究过程中所发挥的作用愈发关注，但相关实质性理论却较为匮乏。这使得探索性中介分析（mediation analysis）成为必要，而结构方程模型（structural equation modeling）正是该类分析的基准方法。然而，当变量数量多于观测样本量时，该方法无法开展中介分析。一种可行方案是构建一系列单变量中介模型（univariate mediation models），但该方案错误地假设所有中介变量之间相互独立；另一种方案是采用正则化（regularization）方法，但现有实现方式可能会导致较高的假阳性率（false-positive rates）。本文提出一种融合过滤与正则化思想的混合方法——逐坐标中介过滤法（Coordinate-wise Mediation Filter），该方法会基于已选中的其他中介变量开展条件过滤操作。我们通过模拟实验证明，相较于现有方法，该方法的表现更为优异。最后，本文提供了一个实证案例，展示了该方法在表观遗传学研究中的具体应用方式。