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.

翻译：本文提出了一种新颖的方法论，通过结合改进的最小绝对收缩与选择算子（LASSO）和通路LASSO，在高维中介模型中检测多重生物标志物。该方法有效解决了直接效应高估问题，该问题可能导致对具有非零间接效应的中介变量识别不准确。为缓解这种高估现象并提高检测中介变量的真阳性率，本文引入了两种对$L_1$范数惩罚项的约束条件。通过在不同场景下的大量模拟实验，证明了所提方法论的有效性，突显了其在各种条件下的稳健性和可靠性。此外，本文还引入了一种基于确定独立筛选的维度缩减最优阈值选择流程，从而提高了真实生物标志物检测的准确性，并最终获得一个既稳健又适用于实际应用的模型。为说明该方法论的实用价值，我们将结果应用于一项涉及内化精神病理患者的研究数据集，展示了其在临床环境中的适用性。总体而言，该方法论标志着高维中介模型中生物标志物检测领域的重大进展，为研究和临床实践提供了具有前景的启示。