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

Abstract

In Alzheimer's disease research, a clinically important question is how much longer individuals would remain dementia-free beyond a given time under different baseline amyloid statuses. We address this question using observational data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) treating baseline amyloid status as the exposure. Estimation is challenging because amyloid status is confounded, time to dementia onset is heterogeneous and heavily right censored, and the target population depends on joint potential event times. At each time point, we consider the always-survivor principal stratum comprising individuals who would remain dementia-free under both amyloid status and estimate quantile contrasts in residual time to dementia onset. We model the joint distribution of event time, exposure, and baseline covariates using an enriched Dirichlet process mixture and conduct posterior inference via Bayesian g-computation. The framework accommodates partially observed covariates under a within-subcluster missing-at-random assumption, estimates contrasts across multiple time points and quantiles from one posterior fit and supports sensitivity analyses for unmeasured confounding, cross-world dependence, and informative censoring. Simulations show favorable finite-sample performance under heterogeneity and heavy censoring. In ADNI, residual time to dementia onset was shorter under elevated than non-elevated baseline amyloid status, both overall and within baseline subgroups.