Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.

Multisite analysis of electronic health record (EHR) data presents unique opportunities for studying disease progression in real-world settings. However, privacy concerns, communication costs, and site-level heterogeneity pose significant challenges for analyzing longitudinal data. We introduce PEAL (Privacy-preserving Efficient Aggregation for Longitudinal data), a novel federated learning algorithm for fitting multi-level linear mixed-effects models with spline basis terms for nonlinear temporal trends. PEAL requires only a single-round transfer of summary statistics and produces results identical to using pooled individual participant data. Simulation studies demonstrate that PEAL accurately recovers fixed effects and variance components under realistic multi-level structures. We applied PEAL to real-world longitudinal datasets of systemic sclerosis patients from the Johns Hopkins and University of Pittsburgh Scleroderma Centers. This application shows our algorithm captures reasonable disease trajectories. Overall, PEAL provides a practical solution for distributed research networks studying rare diseases and time-evolving clinical outcomes by enabling lossless, communication-efficient, and privacy-preserving modeling.