SynthesisScientific reports2022
ODACH: a one-shot distributed algorithm for Cox model with heterogeneous multi-center data.
Synthesis in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- One-shot distributed algorithms for addressing heterogeneity in competing risks data across clinical sites.Journal of biomedical informatics · 2024Pooled 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
- 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
- Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.NPJ digital medicine · 2025Review
- A communication-efficient federated learning algorithm to assess racial disparities in post-transplantation survival time.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- FedECA: federated external control arms for causal inference with time-to-event data in distributed settings.Nature communications · 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
- 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
- Privacy-preserving analysis of time-to-event data under nested case-control sampling.Statistical methods in medical research · 2024Article
- Federated and distributed learning applications for electronic health records and structured medical data: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Padé approximant meets federated learning: A nearly lossless, one-shot algorithm for evidence synthesis in distributed research networks with rare outcomes.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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
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.