Collaborative Quantile Treatment Effect Estimation for Distributed Alzheimer's Research Data

Music City Center 

Our recent analyses of the NACC data have revealed substantial response-dependent heterogeneity in the causal effects of repurposed drugs (e.g., metformin), social indicators (e.g., living alone), and genetic factors (e.g., APOE genotype) in AD patients. This heterogeneity in response highlights the need for quantile treatment effect (QTE) estimation, which captures how treatment effects vary across the distribution of clinical outcomes—beyond what average treatment effect (ATE) approaches can reveal.
However, estimating QTEs in modern AD research presents significant methodological and computational challenges. Large-scale observational, biomarker, and neuroimaging datasets are often distributed across sites, with privacy constraints and limited data-sharing infrastructure preventing centralized analysis.

We introduce SCQTE, a sequential collaborative method for scalable and privacy-preserving QTE estimation across distributed data environments. SCQTE accommodates both conditional and unconditional QTEs, requires only one or two rounds of inter-site communication, and achieves estimation accuracy equivalent to oracle estimators using pooled data. This work provides a practical solution for collaborative causal inference in aging and dementia research at scale.