Academic Journal
Bayesian tele-connected spatial clustering of multivariate spatial data with applications to disease-mapping.
| Τίτλος: | Bayesian tele-connected spatial clustering of multivariate spatial data with applications to disease-mapping. |
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| Συγγραφείς: | Bhattacharyya S; Department of Statistics, Texas A&M University, College Station, TX 77843, United States., Sang H; Department of Statistics, Texas A&M University, College Station, TX 77843, United States., Mallick B; Department of Statistics, Texas A&M University, College Station, TX 77843, United States. |
| Πηγή: | Biometrics [Biometrics] 2026 Jul 01; Vol. 82 (3). |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Oxford University Press Country of Publication: England NLM ID: 0370625 Publication Model: Print Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE |
| Imprint Name(s): | Publication: March 2024- : [Oxford] : Oxford University Press Original Publication: Alexandria Va : Biometric Society |
| Ιατρικοί όροι (MeSH): | Spatial Analysis*, Prostatic Neoplasms/mortality ; United States/epidemiology ; Bayes Theorem ; Cluster Analysis ; Clustering Algorithms ; Computer Simulation ; Multivariate Analysis ; Humans ; Algorithms ; Monte Carlo Method ; Male ; Markov Chains ; Models, Statistical |
| Περίληψη: | Spatial clustering is crucial in disease mapping by identifying subregions with different patterns of disease incidence or mortality. This study proposes a novel Bayesian spatial clustering method for multivariate spatial disease data, which allows for understanding geographic variations of multivariate disease patterns while accounting for both spatial information and dependence among multiple disease measurements. We develop a new random tele-connected graph partition model with an unknown number of clusters, which is capable of encouraging locally contiguous clusters and allowing for remote subregions to be clustered together. We use this prior in a Bayesian hierarchical model to detect spatial clusters and estimate cluster-specific disease patterns and dependence across the multivariate disease variables. We develop a tailored Markov chain Monte Carlo (MCMC) algorithm for posterior inference, utilizing efficient doubly split-merge samplers taking advantage of graph algorithms. We illustrate our method with simulation studies and apply it to investigate the clustering patterns of county-level prostate cancer mortality rate decline across six southern U.S. states from 1985 to 2014. (© The Author(s) 2026. Published by Oxford University Press on behalf of The International Biometric Society.) |
| Grant Information: | NSF DMS-2220231 NSF; NSF SES-2521573 NSF; United States GM NIGMS NIH HHS; R01GM163238 United States NH NIH HHS |
| Contributed Indexing: | Keywords: Bayesian model-based clustering; boundary detection; cancer mortality; graph partitions; multivariate spatial variables; spatial clustering |
| Entry Date(s): | Date Created: 20260717 Date Completed: 20260717 Latest Revision: 20260726 |
| Update Code: | 20260726 |
| PubMed Central ID: | PMC13377533 |
| DOI: | 10.1093/biomtc/ujag128 |
| PMID: | 42466843 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1541-0420 |
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| DOI: | 10.1093/biomtc/ujag128 |