ArticleJournal of the American Medical Informatics Association : JAMIA
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers, 4 of them syntheses that pooled it.
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Who cites it
94 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Estimating individualized treatment effects using an individual participant data meta-analysis.BMC medical research methodology · 2024Pooled it
- Distributed Quasi-Poisson regression algorithm for modeling multi-site count outcomes in distributed data networks.Journal of biomedical informatics · 2022Pooled it
- Privacy technology to support data sharing for comparative effectiveness research: a systematic review.Medical care · 2013Pooled it
- Clinical research data warehouse governance for distributed research networks in the USA: a systematic review of the literature.Journal of the American Medical Informatics Association : JAMIAPooled it
- PDA (Privacy-Preserving Distributed Algorithms) in action: ten principles for high-quality multi-site clinical evidence generation.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Federated target trial emulation for time-to-event outcomes via POLARIS: Pooled-equivalent One-shot Likelihood Aggregation for Real-world Inference in Survival.Research square · 2026Article
- Federated analytics for non-communicable disease surveillance in the European health data space: a scoping review and conceptual framework.Frontiers in public health · 2026Article
- A communication-efficient federated learning algorithm to assess racial disparities in post-transplantation survival time.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.NPJ digital medicine · 2025Article
- Communication-efficient federated learning of temporal effects on opioid use disorder with data from distributed research networks.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Distributed cross-learning for equitable federated models - privacy-preserving prediction on data from five California hospitals.Nature communications · 2025Article
- Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.Statistics in medicine · 2024Article
- Distributed Statistical Analyses: A Scoping Review and Examples of Operational Frameworks Adapted to Health Analytics.JMIR medical informatics · 2024Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- Federated Learning in Healthcare: A Benchmark Comparison of Engineering and Statistical Approaches for Structured Data Analysis.Health data science · 2024Article
- Learning from vertically distributed data across multiple sites: An efficient privacy-preserving algorithm for Cox proportional hazards model with variable selection.Journal of biomedical informatics · 2024Article
- COMMUTE: Communication-efficient transfer learning for multi-site risk prediction.Journal of biomedical informatics · 2023Article
- Article
- Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Quorum-based model learning on a blockchain hierarchical clinical research network using smart contracts.International journal of medical informatics · 2023Article
34 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveThe classification of complex or rare patterns in clinical and genomic data requires the availability of a large, labeled patient set. While methods that operate on large, centralized data sources have been extensively used, little attention has been paid to understanding whether models such as binary logistic regression (LR) can be developed in a distributed manner, allowing researchers to share models without necessarily sharing patient data. MATERIAL AND
methodsInstead of bringing data to a central repository for computation, we bring computation to the data. The Grid Binary LOgistic REgression (GLORE) model integrates decomposable partial elements or non-privacy sensitive prediction values to obtain model coefficients, the variance-covariance matrix, the goodness-of-fit test statistic, and the area under the receiver operating characteristic (ROC) curve.
resultsWe conducted experiments on both simulated and clinically relevant data, and compared the computational costs of GLORE with those of a traditional LR model estimated using the combined data. We showed that our results are the same as those of LR to a 10(-15) precision. In addition, GLORE is computationally efficient. LIMITATION: In GLORE, the calculation of coefficient gradients must be synchronized at different sites, which involves some effort to ensure the integrity of communication. Ensuring that the predictors have the same format and meaning across the data sets is necessary.
conclusionThe results suggest that GLORE performs as well as LR and allows data to remain protected at their original sites.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.