ArticleFrontiers in digital health2025
Horizontal federated learning and assessment of Cox models.
Article in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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Who cites it
3 citing papers in PubMed.
- Effects of age and sex on all-cause mortality among NHANES participants with metabolic syndrome and rheumatoid arthritis.BMC cardiovascular disorders · 2026Article
- Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach.International journal of population data science · 2026Article
- Association between intrinsic capacity, changes in intrinsic capacity, and cardiometabolic multimorbidity: results from three prospective cohort studies.Frontiers in endocrinology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Cox Proportional Hazards model is a widely used method for survival analysis in medical research. However, training an accurate model requires access to a sufficiently large dataset, which is often challenging due to data fragmentation. A potential solution is to combine data from multiple medical institutions, but privacy constraints typically prevent direct data sharing. Federated learning offers a privacy-preserving alternative by allowing multiple parties to collaboratively train a model without exchanging raw data. In this work, we develop algorithms for training Cox models in a federated setting, leveraging survival stacking to facilitate distributed learning. In addition, we introduce a novel secure computation of Schoenfeld residuals, a key diagnostic tool for validating the Cox model. We provide an open-source implementation of our approach and present empirical results that demonstrate the accuracy and benefits of federated Cox regression.
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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.