18: Dynamic Functional Mediation Model in Geospatial Studies

Chao Huang Co-Author
University of Georgia
 
Miyeon Yeon First Author
The University of Tennessee Health Science Center
 
Miyeon Yeon Presenting Author
The University of Tennessee Health Science Center
 
Monday, Aug 4: 2:00 PM - 3:50 PM
0783 
Contributed Posters 
Music City Center 
Although the causal effects can be affected by the interaction effects between exposure and confounders, it is difficult to capture in a mediator model. Some function-on-scalar models with functional responses and scalar covariates have studied the dynamic relationship between the functional responses and covariates or the relationship between the functional responses depending on the time and some covariates, but the interaction effects between exposure and confounders could not be found. To explore the dynamic effects of the single index including the dynamic confounders, we propose a dynamic functional mediation model. The simulation studies evaluate the performance and validity of the proposed model with the estimation and inference procedures for causal estimands using the wild bootstrap method. We apply our approach to country-level datasets with an adjustment of the dependency between countries by estimating the spatial dependence. Thus, the pathways of individual indirect effects from exposure to outcome through the geospatial mediator can be investigated without linear assumption and dependency issues.

Keywords

COVID-19

dynamic interaction semiparametric function-on-scalar model

mediation analysis

penalized scalar-on-function linear regression 

Abstracts


Main Sponsor

Section on Statistics in Epidemiology