SynthesisBiostatistics (Oxford, England)2023
Distributed Cox proportional hazards regression using summary-level information.
Synthesis in Biostatistics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07431515 (The Long-term Risk of Cardiovascular Disease and Mortality in Living Kidney Donors), which is not on this map. Cited by 9 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.
The Long-term Risk of Cardiovascular Disease and Mortality in Living Kidney Donors
Who cites it
9 citing papers in PubMed.
- Estimating Optimal Site-Specific Individualized Treatment Rules With Limited Data Sharing.Statistics in medicine · 2026Article
- Bayesian federated inference for survival models.Journal of applied statistics · 2026Article
- Collaborative Inference for Accelerated Failure Time Model Using Clinical Center-Level Summary Statistics.Statistics in medicine · 2025Article
- FedECA: federated external control arms for causal inference with time-to-event data in distributed settings.Nature communications · 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
- Association of Urinary Metals With Cardiovascular Disease Incidence and All-Cause Mortality in the Multi-Ethnic Study of Atherosclerosis (MESA).Circulation · 2024Article
- Centralized and Federated Models for the Analysis of Clinical Data.Annual review of biomedical data science · 2024Review
- Heterogeneity-aware Clustered Distributed Learning for Multi-source Data Analysis.Journal of machine learning research : JMLR · 2024Article
- Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.Journal of the American Statistical Association · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Individual-level data sharing across multiple sites can be infeasible due to privacy and logistical concerns. This article proposes a general distributed methodology to fit Cox proportional hazards models without sharing individual-level data in multi-site studies. We make inferences on the log hazard ratios based on an approximated partial likelihood score function that uses only summary-level statistics. This approach can be applied to both stratified and unstratified models, accommodate both discrete and continuous exposure variables, and permit the adjustment of multiple covariates. In particular, the fitting of stratified Cox models can be carried out with only one file transfer of summary-level information. We derive the asymptotic properties of the proposed estimators and compare the proposed estimators with the maximum partial likelihood estimators using pooled individual-level data and meta-analysis methods through simulation studies. We apply the proposed method to a real-world data set to examine the effect of sleeve gastrectomy versus Roux-en-Y gastric bypass on the time to first postoperative readmission.
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.