ArticleNature communications2022
DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.Scientific reports · 2022Pooled 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
- Estimating Optimal Site-Specific Individualized Treatment Rules With Limited Data Sharing.Statistics in medicine · 2026Article
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Accelerating discovery of cancer causes for prevention in the era of rising early-onset cancers.Cell · 2026Review
- Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.NPJ digital medicine · 2025Article
- COLA-GLM: collaborative one-shot and lossless algorithms of generalized linear models for decentralized observational healthcare data.NPJ digital medicine · 2025Article
- A critical assessment of matching-adjusted indirect comparisons in relation to target populations.Research synthesis methods · 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
- FAItH: Federated Analytics and Integrated Differential Privacy with Clustering for Healthcare Monitoring.Scientific reports · 2025Article
- Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach.Statistics in medicine · 2024Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- BUILDING A DOSE TOXO-EQUIVALENCE MODEL FROM A BAYESIAN META-ANALYSIS OF PUBLISHED CLINICAL TRIALS.The annals of applied statistics · 2023Article
- Representing and utilizing clinical textual data for real world studies: An OHDSI approach.Journal of biomedical informatics · 2023Article
- Multi-Task Learning with Summary Statistics.Advances in neural information processing systems · 2023Article
- dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites.Scientific reports · 2022Article
- Distributed learning for heterogeneous clinical data with application to integrating COVID-19 data across 230 sites.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
28 authors.
Funding
Abstract
Linear mixed models are commonly used in healthcare-based association analyses for analyzing multi-site data with heterogeneous site-specific random effects. Due to regulations for protecting patients' privacy, sensitive individual patient data (IPD) typically cannot be shared across sites. We propose an algorithm for fitting distributed linear mixed models (DLMMs) without sharing IPD across sites. This algorithm achieves results identical to those achieved using pooled IPD from multiple sites (i.e., the same effect size and standard error estimates), hence demonstrating the lossless property. The algorithm requires each site to contribute minimal aggregated data in only one round of communication. We demonstrate the lossless property of the proposed DLMM algorithm by investigating the associations between demographic and clinical characteristics and length of hospital stay in COVID-19 patients using administrative claims from the UnitedHealth Group Clinical Discovery Database. We extend this association study by incorporating 120,609 COVID-19 patients from 11 collaborative data sources worldwide.
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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.