29: Measurement Error Models for Mediation Analysis

Music City Center 

Mediation pathway often involves multiple mediators and selecting true mediators is an essential step in addressing key scientific questions. We propose a novel adoption of the Measurement Error Model (MEM) framework in mediation analysis for mediator selection. The MEM framework enables variable selection by deliberately introducing measurement errors to predictors, identifying variables whose predictive utility is most sensitive to such perturbations. When introducing a certain amount of measurement error into the mediation pathway and distributing across multiple mediators, the optimization of the joint MEM likelihood will assign the majority of measurement errors to mediators that are not important in the mediation system while maintaining important mediators less impacted, effectively achieving variable selection. This approach is readily to extend naturally to path selection for identifying true mediators. We demonstrate the efficacy of the proposed method through extensive simulations across various scenarios, comparing its performance with existing approaches.

Mediation Analysis

Measurement Error Models 

Section on Statistics in Epidemiology