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

Abstract Number:

1818 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Chengyun Li (1), Yi Zhao (2), Wanzhu Tu (2)

Institutions:

(1) N/A, N/A, (2) Indiana University School of Medicine, N/A

Co-Author(s):

Yi Zhao  
Indiana University School of Medicine
Wanzhu Tu  
Indiana University School of Medicine

First Author:

Chengyun Li  
N/A

Presenting Author:

Chengyun Li  
N/A

Abstract Text:

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.

Keywords:

Causal Mediation Analysis|Heterogeneous Treatment Effects|Generalized LASSO|High-Dimensional Covariates|Natural Direct and Indirect Effects|Personalized Medicine

Sponsors:

Biometrics Section

Tracks:

High Dimensional Regression

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