Sparse principal component based high-dimensional mediation analysis

Yi Zhao, Martin A. Lindquist and Brian S. Caffo

Computational Statistics & Data Analysis, 2020, vol. 142, issue C

Abstract: 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.

Keywords: Functional magnetic resonance imaging; Mediation analysis; Regularized regression; Structural equation model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:142:y:2020:i:c:s0167947319301902

DOI: 10.1016/j.csda.2019.106835

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