Bayesian Nonparametric Causal Inference for Quantile Residual Life: Application to Alzheimer's Disease

Thomas M. Menino Convention & Exhibition Center 

In Alzheimer's disease research, an important clinical question for individuals still dementia-free at a given follow-up time is how much longer they will remain so. We address this question in the Alzheimer's Disease Neuroimaging Initiative (ADNI), focusing on baseline amyloid status as the exposure. Estimation is challenging because amyloid status is observed rather than randomized, requiring adjustment for confounding, and because time to dementia onset is heterogeneous and heavily right-censored. To address these challenges with clinically interpretable summaries, we focus on quantiles of the residual time to dementia, whose contrasts across amyloid groups quantify how prognosis differs by exposure. We estimate causal contrasts in quantile residual life using a Bayesian nonparametric enriched Dirichlet process mixture model for the joint distribution of event times, exposure, and baseline covariates, with inference via Bayesian g-computation. The approach accommodates ignorable missing baseline covariates through data augmentation, supports inference across clinically relevant landmark times, and allows sensitivity analysis for residual unmeasured confounding. Simulation studies show good performance under complex heterogeneity and heavy censoring. In ADNI, among individuals still dementia-free at relevant landmark times, elevated versus non-elevated baseline amyloid yielded shorter quantiles of remaining dementia-free time, overall and within baseline diagnostic subgroups.

Alzheimer's disease

Bayesian nonparametrics

Causal inference

Quantile residual life

Sensitivity analysis 

Biometrics Section