Sparse Principal Component based High-Dimensional Mediation Analysis

Abstract

Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. With multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. Huang and Pan (2016) introduced a principal component analysis (PCA) based approach to address this challenge, in which the transformed mediators are conditionally independent given the orthogonality of the PCs. However, the transformed mediator PCs, which are linear combinations of original mediators, are difficult to interpret. In this study, we propose a sparse high-dimensional mediation analysis approach by adopting the sparse PCA method introduced by Zou and others (2006) to the mediation setting. We apply the approach to a task-based functional magnetic resonance imaging study, and show that our proposed method is able to detect biologically meaningful results related to the identified mediator.

arXiv comment24 pages, 3 figures, 1 table

References in corpus (7)

• High-dimensional graphs and variable selection with the Lasso
• Identification, Inference and Sensitivity Analysis for Causal Mediation Effects
• The Statistical Analysis of fMRI Data
• The solution path of the generalized lasso
• Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001)
• Dimension reduction based on constrained canonical correlation and variable filtering
• Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediators

Cited by in corpus (3)

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• A Framework for Mediation Analysis with Multiple Exposures, Multivariate Mediators, and Non-Linear Response Models