Journal of the American Medical Informatics Association : JAMIA · 2026
A lossless one-shot distributed algorithm for addressing heterogeneity in multi-site generalized linear models.
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Zhang Bingyu, Wu Qiong, Reps Jenna M, Li Lu, Tong Jiayi, Lu Yiwen, Zhang Dazheng, Ramirez-Anguita Juan Manuel, Bian Jiang, Brand Milou T, Falconer Thomas, Mayer Miguel A, Williams Ross D, Chen Yong
Аннотация
OBJECTIVE: We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while relying solely on instituion-level summary information rather than patient-level data.
MATERIALS AND METHODS: Generalized Linear Models (GLMs) are widely used in medical research for analyzing diverse outcome types. In multi-institution settings, we demonstrated that the global likelihood can be reconstructed using only institution-level summary statistics, enabling lossless estimation without accessing individual records. We validated COLA-GLM-H in two real-world studies: (1) an emulated U.S. pediatric centralized network (719,383 patients) evaluating long-term cardiovascular risks following COVID-19, and (2) an internationally decentralized network of 120,429 hospitalized patients from seven databases across three countries assessing risk factors for COVID-19 mortality.
RESULTS: In the centralized network, COLA-GLM-H produced estimates identical to those from pooled analyses. In the decentralized setting, the algorithm effectively integrated heterogeneous data across multiple clinical institutions using a single communication round.
CONCLUSIONS: COLA-GLM-H provides a lossless, communication-efficient, and computation-efficient solution for multi-institutional research using only institution-level summary data. It accounts for between-institution heterogeneity and supports all outcome types within the exponential family, enabling secure, scalable, and accurate analysis in collaborative clinical research.
MATERIALS AND METHODS: Generalized Linear Models (GLMs) are widely used in medical research for analyzing diverse outcome types. In multi-institution settings, we demonstrated that the global likelihood can be reconstructed using only institution-level summary statistics, enabling lossless estimation without accessing individual records. We validated COLA-GLM-H in two real-world studies: (1) an emulated U.S. pediatric centralized network (719,383 patients) evaluating long-term cardiovascular risks following COVID-19, and (2) an internationally decentralized network of 120,429 hospitalized patients from seven databases across three countries assessing risk factors for COVID-19 mortality.
RESULTS: In the centralized network, COLA-GLM-H produced estimates identical to those from pooled analyses. In the decentralized setting, the algorithm effectively integrated heterogeneous data across multiple clinical institutions using a single communication round.
CONCLUSIONS: COLA-GLM-H provides a lossless, communication-efficient, and computation-efficient solution for multi-institutional research using only institution-level summary data. It accounts for between-institution heterogeneity and supports all outcome types within the exponential family, enabling secure, scalable, and accurate analysis in collaborative clinical research.