ArticleStatistics in medicine2021
Multilevel modeling of spatially nested functional data: Spatiotemporal patterns of hospitalization rates in the US dialysis population.
Article in Statistics in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models.Statistics in medicine · 2026Article
- Contrastive Latent Functional Model.Statistics in biosciences · 2026Article
- Bayesian mapping of mortality clusters.Biostatistics (Oxford, England) · 2025Article
- Multivariate varying coefficient spatiotemporal model.Statistics in biosciences · 2024Article
- Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.Lifetime data analysis · 2024Article
- Multivariate spatiotemporal functional principal component analysis for modeling hospitalization and mortality rates in the dialysis population.Biostatistics (Oxford, England) · 2024Article
- Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example.Kidney international reports · 2024Review
- Functional Approaches for Modeling Unfolding Data.Educational and psychological measurement · 2023Article
- Fast Multilevel Functional Principal Component Analysis.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2023Article
- Article
- From multivariate to functional data analysis: fundamentals, recent developments, and emerging areas.Journal of multivariate analysis · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
End-stage renal disease patients on dialysis experience frequent hospitalizations. In addition to known temporal patterns of hospitalizations over the life span on dialysis, where poor outcomes are typically exacerbated during the first year on dialysis, variations in hospitalizations among dialysis facilities across the US contribute to spatial variation. Utilizing national data from the United States Renal Data System (USRDS), we propose a novel multilevel spatiotemporal functional model to study spatiotemporal patterns of hospitalization rates among dialysis facilities. Hospitalization rates of dialysis facilities are considered as spatially nested functional data (FD) with longitudinal hospitalizations nested in dialysis facilities and dialysis facilities nested in geographic regions. A multilevel Karhunen-Loéve expansion is utilized to model the two-level (facility and region) FD, where spatial correlations are induced among region-specific principal component scores accounting for regional variation. A new efficient algorithm based on functional principal component analysis and Markov Chain Monte Carlo is proposed for estimation and inference. We report a novel application using USRDS data to characterize spatiotemporal patterns of hospitalization rates for over 400 health service areas across the US and over the posttransition time on dialysis. Finite sample performance of the proposed method is studied through simulations.
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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.