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Mediation Analysis with Ultra-high Dimensional Confounders for the Study on Depression and AD

Abstract Number:

2212 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Paper 

Participants:

Yuexia Zhang (1), Annie Qu (2), Yubai Yuan (3), Qi Xu (4), Fei Xue (5), Kecheng Wei (6)

Institutions:

(1) The University of Texas at San Antonio, N/A, (2) University of California At Irvine, Irvine, CA, (3) Pennsylvania State University, N/A, (4) University of California-Irvine, N/A, (5) Purdue University, N/A, (6) Fudan University, N/A

Co-Author(s):

Annie Qu  
University of California At Irvine
Yubai Yuan  
Pennsylvania State University
Qi Xu  
University of California-Irvine
Fei Xue  
Purdue University
Kecheng Wei  
Fudan University

First Author:

Yuexia Zhang  
The University of Texas at San Antonio

Presenting Author:

Yuexia Zhang  
The University of Texas at San Antonio

Abstract Text:

Depression and Alzheimer's Disease (AD) are both prevalent diseases in older adults. Using the data sets from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we explore whether geriatric depression has a significant average treatment effect on AD and whether the effect is mediated by some important mediators. To estimate these causal effects consistently, we control for ultra-high dimensional potential confounders, including DNA methylation levels. We propose a new ball correlation-based screening method for confounder selection in mediation analysis. To achieve robustness against model misspecification, we utilize a robust mediation analysis framework. Simulation studies show that the proposed method has good finite-sample performance in terms of confounder and mediator selection, effect estimation, and inference. In the real data analysis, we find that geriatric depression has a significantly positive causal effect on AD. We also propose new prevention and treatment strategies for geriatric depression and AD through changing the selected confounders and mediators.

Keywords:

causal inference|mediation analysis|Alzheimer’s disease|geriatric depression |ultra-high dimensional potential confounders|

Sponsors:

Biometrics Section

Tracks:

Model/Variable Selection

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