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Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless
Jiayi Tong1,2,3, Jenna M Reps4,5,6, Chongliang Luo7
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. jtong20@jhu.edu.
NPJ Digital Medicine
|July 18, 2025
Summary
A new algorithm, Collaborative One-shot Lossless Algorithm for Generalized Linear Mixed Models (COLA-GLMM), enables accurate, low-burden, and privacy-preserving multi-site research using only summary statistics. This method streamlines data analysis across healthcare networks while protecting sensitive patient information.
Area of Science:
- Health Informatics
- Biostatistics
- Computational Biology
Background:
- Real-world data (RWD) adoption fuels healthcare research networks.
- Multi-site analyses face administrative and data privacy hurdles.
- Existing methods lack efficiency and robust privacy.
Purpose of the Study:
- Introduce a novel algorithm for efficient and secure multi-site data analysis.
- Address the limitations of current distributed research network methodologies.
- Enable accurate statistical modeling without direct data sharing.
Main Methods:
- Developed the Collaborative One-shot Lossless Algorithm for Generalized Linear Mixed Models (COLA-GLMM).
- Incorporated homomorphic encryption for enhanced privacy of summary statistics.
- Validated through simulations and real-world application on international datasets.
Main Results:
- COLA-GLMM achieved near-exact agreement with pooled data in parameter estimation (7.8×10⁻⁶%-3.0% difference).
- The algorithm operates in a single communication round, reducing overhead.
- Demonstrated effectiveness in identifying COVID-19 mortality risk factors.
Conclusions:
- COLA-GLMM offers a breakthrough for accurate, low-burden, and privacy-preserving multi-site research.
- The algorithm facilitates collaborative analysis of distributed health data.
- Enhanced privacy features mitigate risks associated with summary statistics.
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