A0657
Title: Mediation analysis with latent factors using simultaneous group-wise and parameter-wise penalization
Authors: Xizhen Cai - Williams College (United States) [presenting]
Qing Wang - Wellesley College (United States)
Yeying Zhu - University of Waterloo (Canada)
Abstract: Mediation analysis aims to uncover the underlying mechanism of how an exposure variable affects the outcome of interest through one or more mediating variables. In the event that the number of candidate mediators is large, variable selection or dimension reduction techniques are often utilized to reduce the dimension of the initial set of mediators. The proposed latent variable approach is discussed using sparse factor analysis with both group-wise and parameter-wise penalization to remove irrelevant candidate mediators and estimate the latent factors simultaneously. After the low-dimensional latent mediating factors are obtained, the direct and indirect effects can be estimated and tested from a multivariate mediation model. To demonstrate the practical applications of the proposed methodology, real-world applications are discussed with a weight behavior dataset and an environmental dataset.
