title: Two-level spatially structured models in spatio-temporal disease mapping.
abstract: This work focuses on extending some classical spatio-temporal models in disease mapping. The objective is to present a family of flexible models to analyze real data naturally organized in two different levels of spatial aggregation like municipalities within health areas or provinces, or counties within states. Model fitting and inference will be carried out using integrated nested Laplace approximations. The performance of the new models compared to models including a single spatial random effect is assessed by simulation. Results show good behavior of the proposed two-level spatially structured models in terms of several criteria. Brain cancer mortality data in the municipalities of two regions in Spain will be analyzed using the new model proposals. It will be shown that a model with two-level spatial random effects overcomes the usual single-level models.
authors: Department of Statistics and Operations Research, Public University of Navarre, Spain Institute for Advanced Materials (InaMat), Public University of Navarre, Spain lola@unavarra.es.
authors: María Dolores
authors: MD
authors: Ugarte
authors: Department of Statistics and Operations Research, Public University of Navarre, Spain Institute for Advanced Materials (InaMat), Public University of Navarre, Spain.
authors: Aritz
authors: A
authors: Adin
authors: Department of Statistics and Operations Research, Public University of Navarre, Spain Institute for Advanced Materials (InaMat), Public University of Navarre, Spain Research Network on Health Services in Chronic Diseases (REDISSEC), Spain.
authors: Tomás
authors: T
authors: Goicoa
mesh_terms: D001932:Brain Neoplasms / Q000401:mortality*; D006801:Humans; D013030:Spain / Q000453:epidemiology; D062211:Spatio-Temporal Analysis
keywords: Brain cancer mortality data; disease mapping; integrated nested Laplace approximations