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

Oregon Convention Center 

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.

causal inference

mediation analysis

Alzheimer’s disease

geriatric depression

ultra-high dimensional potential confounders 

Biometrics Section