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Updated: Sep 14, 2025

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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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.

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Summary
This summary is machine-generated.

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.

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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.