Pathway Lasso: Pathway Estimation and Selection with High-Dimensional Mediators.

ABSTRACT: In many scientific studies, it becomes increasingly important to delineate the pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as the product of coefficients. However, it becomes unstable and computationally challenging to fit such models with high-dimensional mediators. This paper proposes a sparse mediation model using a regularized SEM approach, where sparsity means that a small number of mediators have a nonzero mediation effect between a treatment and an outcome. To address the model selection challenge, we innovate by introducing a new penalty called Pathway Lasso. This penalty function is a convex relaxation of the non-convex product

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC9269990 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature