ArticleJournal of the American Medical Informatics Association : JAMIA2022
dPQL: a lossless distributed algorithm for generalized linear mixed model with application to privacy-preserving hospital profiling.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- D3MI: an efficient and powerful federated imputation method for bias reduction in the analysis of distributed incomplete data by accounting for within-site correlation and between-site heterogeneity.BMC medical informatics and decision making · 2026Article
- 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
- Distributed fusion R-learner of heterogeneous treatment effect using distributed medicaid data.Biometrics · 2026Article
- dGAMLSS: an exact, distributed algorithm to fit Generalized Additive Models for Location, Scale, and Shape for privacy-preserving population reference charts.Bioinformatics (Oxford, England) · 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
- Unlocking efficiency in real-world collaborative studies: a multi-site international study with one-shot lossless GLMM algorithm.NPJ digital medicine · 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
- 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
- Federated generalized linear mixed models for collaborative genome-wide association studies.iScience · 2023Article
- A Review of Privacy Enhancement Methods for Federated Learning in Healthcare Systems.International journal of environmental research and public health · 2023Review
- Article
- Federated learning algorithms for generalized mixed-effects model (GLMM) on horizontally partitioned data from distributed sources.BMC medical informatics and decision making · 2022Article
- The imperative of applying ethical perspectives to biomedical and health informatics.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
- DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.Nature communications · 2022Article
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9 authors.
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Abstract
objectiveTo develop a lossless distributed algorithm for generalized linear mixed model (GLMM) with application to privacy-preserving hospital profiling. MATERIALS AND
methodsThe GLMM is often fitted to implement hospital profiling, using clinical or administrative claims data. Due to individual patient data (IPD) privacy regulations and the computational complexity of GLMM, a distributed algorithm for hospital profiling is needed. We develop a novel distributed penalized quasi-likelihood (dPQL) algorithm to fit GLMM when only aggregated data, rather than IPD, can be shared across hospitals. We also show that the standardized mortality rates, which are often reported as the results of hospital profiling, can also be calculated distributively without sharing IPD. We demonstrate the applicability of the proposed dPQL algorithm by ranking 929 hospitals for coronavirus disease 2019 (COVID-19) mortality or referral to hospice that have been previously studied.
resultsThe proposed dPQL algorithm is mathematically proven to be lossless, that is, it obtains identical results as if IPD were pooled from all hospitals. In the example of hospital profiling regarding COVID-19 mortality, the dPQL algorithm reached convergence with only 5 iterations, and the estimation of fixed effects, random effects, and mortality rates were identical to that of the PQL from pooled data.
conclusionThe dPQL algorithm is lossless, privacy-preserving and fast-converging for fitting GLMM. It provides an extremely suitable and convenient distributed approach for hospital profiling.
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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.