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 effectiveness of the proposed methodology is demonstrated via extensive simulations across various scenarios and compared against other popular mediation methods, 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 and another dataset involving patients with late-life depression, 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.