title: A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.
abstract: Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models.
authors: NOVA Information Management School, Universidade Nova de Lisboa, Lisboa, Portugal D2011073@novaims.unl.pt.
authors: Helena
authors: H
authors: Baptista
authors: NOVA Information Management School, Universidade Nova de Lisboa, Lisboa, Portugal.
authors: Jorge M
authors: JM
authors: Mendes
authors: Epidemiology and Biostatistics, School of Population and Public Health, University of British Columbia, Vancouver, Canada.
authors: Ying C
authors: YC
authors: MacNab
authors: Chronic Diseases Research Center (CEDOC) and Department of Mental Health, NOVA Medical School, Universidade Nova de Lisboa, Lisboa, Portugal.
authors: Miguel
authors: M
authors: Xavier
authors: Chronic Diseases Research Center (CEDOC) and Department of Mental Health, NOVA Medical School, Universidade Nova de Lisboa, Lisboa, Portugal.
authors: José
authors: J
authors: Caldas-de-Almeida
mesh_terms: D000293:Adolescent; D000328:Adult; D000437:Alcoholism / Q000453:epidemiology; D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008048:Lip Neoplasms / Q000453:epidemiology; D008297:Male; D008390:Markov Chains; D016011:Normal Distribution; D011174:Portugal / Q000453:epidemiology; D012307:Risk Factors; D012606:Scotland / Q000453:epidemiology; D055815:Young Adult
keywords: Besag–York–Mollié model; Gaussian Markov random field and Gaussian random field models; Neighbourhood matrix; alcohol abuse disorder; disease mapping; similarity-based smoothing