High-dimensional order-free multivariate spatial disease mapping
Fecha
2023Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
Impacto
|
10.1007/s11222-023-10263-x
Resumen
Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains
limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number
of areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth
mortality (or incidenc ...
[++]
Despite the amount of research on disease mapping in recent years, the use of multivariate models for areal spatial data remains
limited due to difficulties in implementation and computational burden. These problems are exacerbated when the number
of areas is very large. In this paper, we introduce an order-free multivariate scalable Bayesian modelling approach to smooth
mortality (or incidence) risks of several diseases simultaneously. The proposal partitions the spatial domain into smaller
subregions, fits multivariate models in each subdivision and obtains the posterior distribution of the relative risks across the
entire spatial domain. The approach also provides posterior correlations among the spatial patterns of the diseases in each
partition that are combined through a consensus Monte Carlo algorithm to obtain correlations for the whole study region.
We implement the proposal using integrated nested Laplace approximations (INLA) in the R package bigDM and use it to
jointly analyse colorectal, lung, and stomach cancer mortality data in Spanish municipalities. The new proposal allows for the
analysis of large datasets and yields superior results compared to fitting a single multivariate model. Additionally, it facilitates
statistical inference through local homogeneous models, which may be more appropriate than a global homogeneous model
when dealing with a large number of areas. [--]
Materias
Bayesian inference,
High-dimensional data,
Scalable models,
Spatial epidemiology
Editor
Springer
Publicado en
Statistics and Computing, (2023) 33:104
Departamento
Universidad Pública de Navarra. Departamento de Estadística, Informática y Matemáticas /
Nafarroako Unibertsitate Publikoa. Estatistika, Informatika eta Matematika Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute for Advanced Materials and Mathematics - INAMAT2
Versión del editor
Entidades Financiadoras
This work has been supported by the project PID2020-113125RB-I00/MCIN/AEI/10.13039/501100011033. It has also been partially funded by the Public University of Navarra (project PJUPNA2001). Open Access funding provided by Universidad Pública de Navarra.