ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
22 04 2022
22 04 2022
Historique:
received:
17
07
2021
accepted:
28
02
2022
entrez:
23
4
2022
pubmed:
24
4
2022
medline:
27
4
2022
Statut:
epublish
Résumé
We developed a One-shot Distributed Algorithm for Cox proportional-hazards model to analyze Heterogeneous multi-center time-to-event data (ODACH) circumventing the need for sharing patient-level information across sites. This algorithm implements a surrogate likelihood function to approximate the Cox log-partial likelihood function that is stratified by site using patient-level data from a lead site and aggregated information from other sites, allowing the baseline hazard functions and the distribution of covariates to vary across sites. Simulation studies and application to a real-world opioid use disorder study showed that ODACH provides estimates close to the pooled estimator, which analyzes patient-level data directly from all sites via a stratified Cox model. Compared to the estimator from meta-analysis, the inverse variance-weighted average of the site-specific estimates, ODACH estimator demonstrates less susceptibility to bias, especially when the event is rare. ODACH is thus a valuable privacy-preserving and communication-efficient method for analyzing multi-center time-to-event data.
Identifiants
pubmed: 35459767
doi: 10.1038/s41598-022-09069-0
pii: 10.1038/s41598-022-09069-0
pmc: PMC9033863
doi:
Types de publication
Journal Article
Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6627Subventions
Organisme : Patient-Centered Outcomes Research Institute
ID : ME-2019C3-18315
Pays : United States
Organisme : NIH HHS
ID : RF1 AG061351
Pays : United States
Informations de copyright
© 2022. The Author(s).
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