Primary Submission Category: Mediation
Causal Mediation Analysis with Ultra-high Dimensional Potential Confounders for the Study on Geriatric Depression and Alzheimer’s Disease
Authors: Yuexia Zhang, Annie Qu, Yubai Yuan, Qi Xu, Fei Xue, Kecheng Wei,
Presenting Author: Yuexia Zhang*
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.