Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.

Publication date: Jul 19, 2025

The widespread adoption of real-world data has given rise to numerous healthcare-distributed research networks, but multi-site analyses still face administrative burdens and data privacy challenges. In response, we developed a Collaborative One-shot Lossless Algorithm for Generalized Linear Mixed Models (COLA-GLMM), the first-ever algorithm that achieves both lossless and one-shot properties. COLA-GLMM ensures accuracy against the gold standard of pooled data while requiring only summary statistics and completes within a single communication round, eliminating the usual back-and-forth overhead. We further introduced an enhanced version that employs homomorphic encryption to reduce the risks of summary statistics misuse at the coordinating center. The simulation studies showed near-exact agreement with the gold standard in parameter estimation, with relative differences of 7. 8 cD7 10%-3. 0% under various cell suppression settings. We also validated COLA-GLMM on eight international decentralized databases to identify risk factors for COVID-19 mortality. Together, these results show that COLA-GLMM enables accurate, low-burden, and privacy-preserving multi-site research.

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