DLMM as a lossless one-shot algorithm for collaborative multi-site distributed linear mixed models.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
30 03 2022
30 03 2022
Historique:
received:
18
05
2021
accepted:
03
03
2022
entrez:
31
3
2022
pubmed:
1
4
2022
medline:
2
4
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35354802
doi: 10.1038/s41467-022-29160-4
pii: 10.1038/s41467-022-29160-4
pmc: PMC8967932
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1678Subventions
Organisme : NIA NIH HHS
ID : R56 AG069880
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM013519
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA091842
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM012607
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI130460
Pays : United States
Organisme : NIA NIH HHS
ID : R56 AG074604
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG073435
Pays : United States
Informations de copyright
© 2022. The Author(s).
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