ArticleStatistics in medicine2022
A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Joint Models of Two Longitudinal Biomarkers and Clustered Survival Data for Cluster-Informed Dynamic Prediction With Application to Periodontitis.Statistics in medicine · 2026Article
- A Bayesian Multilevel Joint Modeling of Longitudinal and Survival Outcomes in Cluster Randomized Controlled Trial Studies.Statistics in medicine · 2026Article
- A Bayesian Joint Model of Longitudinal Kidney Disease Progression, Recurrent Cardiovascular Events, and Terminal Event in Patients with Chronic Kidney Disease.Statistics in biosciences · 2025Article
- Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.Lifetime data analysis · 2024Article
- Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example.Kidney international reports · 2024Review
- High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions.International journal of statistics in medical research · 2023Article
- A Bayesian multilevel time-varying framework for joint modeling of hospitalization and survival in patients on dialysis.Statistics in medicine · 2022Article
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Authors and funding
6 authors.
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Abstract
Over 782 000 individuals in the United States have end-stage kidney disease with about 72% of patients on dialysis, a life-sustaining treatment. Dialysis patients experience high mortality and frequent hospitalizations, at about twice per year. These poor outcomes are exacerbated at key time periods, such as the fragile period after transition to dialysis. In order to study the time-varying effects of modifiable patient and dialysis facility risk factors on hospitalization and mortality, we propose a novel Bayesian multilevel time-varying joint model. Efficient estimation and inference is achieved within the Bayesian framework using Markov chain Monte Carlo, where multilevel (patient- and dialysis facility-level) varying coefficient functions are targeted via Bayesian P-splines. Applications to the United States Renal Data System, a national database which contains data on nearly all patients on dialysis in the United States, highlight significant time-varying effects of patient- and facility-level risk factors on hospitalization risk and mortality. Finite sample performance of the proposed methodology is studied through simulations.
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