12: Estimation of Heterogeneous Causal Mediation Effects in the Presence of High Dimensional Covariates

Music City Center 

Understanding treatment effects through biological pathways is an essential objective in biomedical investigation. Causal mediation analysis (CMA) provides a useful framework for such inquiries. However, the natural direct effect (NDE) and natural indirect effect (NIE) may depend on specific patient characteristics. To account for such heterogeneity, we include covariate-treatment and mediator-treatment interactions in the outcome model. We relax the strict hierarchical constraint by including interactions without requiring the corresponding main effects. NDE and NIE are then calculated for given values of the covariates. To maintain model parsimony in the presence of high dimensional covariates, we apply generalized LASSO regularization to select key covariate-treatment interactions. Simulation studies show that the method has good performance in selecting the interactions. The method can properly stratify individuals and achieve unbiased estimates for the NDE and NIE. The method represents a step forward in understanding the heterogeneity in the mediation pathway of the treatment within personalized medicine. Data from a real clinical study were used to illustrate the method.

Causal Mediation Analysis

Heterogeneous Treatment Effects

Generalized LASSO

High-Dimensional Covariates

Natural Direct and Indirect Effects

Personalized Medicine 

Biometrics Section