10: Unlocking Efficiency in Real-world Collaborative Studies: A Multi-site International Study with Collaborative One-shot Lossless Algorithm for Generalized Linear Mixed Model

Music City Center 

The widespread adoption of real-world data (RWD) has given rise to numerous centralized and decentralized distributed research networks (DRNs) in health care. However, multi-site analysis using the data within these networks often remains challenging because of administrative burden and privacy concerns, especially in decentralized settings. To address these challenges, we developed the Collaborative One-shot Lossless Algorithm for Generalized Linear Mixed Models (COLA-GLMM), the first-ever algorithm that achieves both lossless and one-shot properties. This novel federated learning algorithm ensures accuracy against the gold standard of pooled patient-level data and offers two additional benefits: (1) it requires only summary statistics, thereby preserving patient privacy, and (2) it delivers results after a single round of communication rather than the multiple back-and-forth communications conventionally required, thereby reducing administrative burden. Additionally, we introduce an enhanced version of COLA-GLMM that employs homomorphic encryption to reduce risks of summary statistics misuse at the level of the coordinating center. We validated our proposed algorithm through simulations and a data application in a real-world study that analyzed decentralized data from eight databases to identify COVID-19 mortality risk factors across multiple sites.