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Dynamic Functional Mediation Model in Geospatial Studies

Abstract Number:

783 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Miyeon Yeon (1), Chao Huang (2)

Institutions:

(1) The University of Tennessee Health Science Center, N/A, (2) University of Georgia, N/A

Co-Author:

Chao Huang  
University of Georgia

First Author:

Miyeon Yeon  
The University of Tennessee Health Science Center

Presenting Author:

Miyeon Yeon  
The University of Tennessee Health Science Center

Abstract Text:

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| |

Sponsors:

Section on Statistics in Epidemiology

Tracks:

Causal Inference

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