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
Qi Xu
University of California-Irvine
First Author:
Presenting Author:
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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