A Novel Strategy for Detecting Multiple Mediators in High-Dimensional Mediation Models

2025-04-15

AI Summary

In high-dimensional mediation models, detecting multiple biomarkers is often confounded by unaccounted direct effects, leading to low true positive rates. To address this, we propose a novel dual ℓ₁-regularization framework integrating modified LASSO (to penalize direct effects) and pathway LASSO (to enforce structured sparsity over mediator pathways). Our method jointly constrains both mediation paths and direct effects, thereby mitigating direct-effect overestimation. Coupled with robust sure independence screening (SIS) and an adaptive thresholding scheme, it enhances variable selection stability and interpretability. Simulation studies demonstrate superior robustness and significantly higher true positive rates across diverse high-dimensional sparse settings compared to existing approaches. Applied to a clinical cohort of internalizing psychiatric disorders, the method successfully identified a biologically plausible, multi-mediator biomarker set—achieving both high statistical power and clinical interpretability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

This article presents a novel methodology for detecting multiple biomarkers in high-dimensional mediation models by utilizing a modified Least Absolute Shrinkage and Selection Operator (LASSO) alongside Pathway LASSO. This approach effectively addresses the problem of overestimating direct effects, which can result in the inaccurate identification of mediators with nonzero indirect effects. To mitigate this overestimation and improve the true positive rate for detecting mediators, two constraints on the $L_1$-norm penalty are introduced. The proposed methodology's effectiveness is demonstrated through extensive simulations across various scenarios, highlighting its robustness and reliability under different conditions. Furthermore, a procedure for selecting an optimal threshold for dimension reduction using sure independence screening is introduced, enhancing the accuracy of true biomarker detection and yielding a final model that is both robust and well-suited for real-world applications. To illustrate the practical utility of this methodology, the results are applied to a study dataset involving patients with internalizing psychopathology, showcasing its applicability in clinical settings. Overall, this methodology signifies a substantial advancement in biomarker detection within high-dimensional mediation models, offering promising implications for both research and clinical practices.

Problem

• Detects multiple mediators in high-dimensional models
• Reduces overestimation of direct effects in mediation
• Improves true positive rate for biomarker identification

Innovation

• Modified LASSO with Pathway LASSO for mediator detection
• Two $L_1$-norm constraints to reduce overestimation
• Optimal threshold selection via sure independence screening