ArticleStatistical analysis and data mining2014
Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease.
Article in Statistical analysis and data mining, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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.
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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
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Development and validation of early warning score system: A systematic literature review.Journal of biomedical informatics · 2020Pooled it
- Multiple Primary Lung Cancers in Surgical Patients: Revisiting Martini and Melamed 50 Years Later.Cancers · 2026Article
- Assessing the comorbidity between asthma and depression through polygenic risk scoring and time-to-event models.BMC medicine · 2026Article
- Neighborhood-level social determinants of suicidality in youth with schizophrenia: An EHR-based study.Schizophrenia research · 2025Article
- MADSurv: An Uncertainty-Aware Framework for Multimodal Cancer Survival Analysis.ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine · 2025Article
- Costs and healthcare use of patients with chronic kidney disease in the Northern Territory, Australia.BMC health services research · 2024Article
- Individualized survival predictions using state space model with longitudinal and survival data.Journal of the Royal Society, Interface · 2024Article
- Learning from vertically distributed data across multiple sites: An efficient privacy-preserving algorithm for Cox proportional hazards model with variable selection.Journal of biomedical informatics · 2024Article
- Dynamic prediction of work status for workers with occupational injuries: assessing the value of longitudinal observations.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- SurvMaximin: Robust federated approach to transporting survival risk prediction models.Journal of biomedical informatics · 2022Article
- Unlocking Potential within Health Systems Using Privacy-Preserving Record Linkage: Exploring Chronic Kidney Disease Outcomes through Linked Data Modelling.Applied clinical informatics · 2022Article
- VERTICOX: Vertically Distributed Cox Proportional Hazards Model Using the Alternating Direction Method of Multipliers.IEEE transactions on knowledge and data engineering · 2022Article
- Prediction models used in the progression of chronic kidney disease: A scoping review.PloS one · 2022Article
- Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Applications of Big Data Analytics to Control COVID-19 Pandemic.Sensors (Basel, Switzerland) · 2021Review
- Learning from local to global: An efficient distributed algorithm for modeling time-to-event data.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Longitudinal Risk Prediction of Chronic Kidney Disease in Diabetic Patients Using a Temporal-Enhanced Gradient Boosting Machine: Retrospective Cohort Study.JMIR medical informatics · 2020Article
- A database for using machine learning and data mining techniques for coronary artery disease diagnosis.Scientific data · 2019Article
- Biases in electronic health record data due to processes within the healthcare system: retrospective observational study.BMJ (Clinical research ed.) · 2018Observational
- High-fidelity phenotyping: richness and freedom from bias.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.
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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.