Zhao Yi, Lindquist Martin A, Caffo Brian S
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
Comput Stat Data Anal. 2020 Feb;142. doi: 10.1016/j.csda.2019.106835. Epub 2019 Sep 3.
Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
因果中介分析旨在量化中介变量在从治疗到结果的因果路径上的中间效应。当处理多个可能存在因果依赖关系的中介变量时，路径效应的可能分解会随着中介变量的数量呈指数增长。一种现有的方法引入了主成分分析（PCA）来应对这一挑战，其依据是在主成分（PC）正交的情况下，变换后的中介变量是条件独立的。然而，变换后的中介变量主成分是原始中介变量的线性组合，可能难以解释。本文提出了一种稀疏高维中介分析方法，该方法将稀疏主成分分析方法应用于中介分析设置中。所提出的方法应用于一项基于任务的功能磁共振成像研究，展示了其检测与已识别中介变量相关的具有生物学意义结果的能力。
