A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.
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...
| Publicado en: | Statistical Methods in Medical Research Vol. 25; no. 4; pp. 1166 - 1185 |
|---|---|
| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Aug2016
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117704363&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117704363 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Aug2016 vid: 25 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 117704363 117704363 NLM27566771 117704363 10.1177/0962280216660407 NLM27566771 117704363 ppf: 1166 ppct: 19 formats: tig: atl: A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping. aug: au: Baptista, Helena Mendes, Jorge M. MacNab, Ying C. Xavier, Miguel Caldas-de-Almeida, José affil: NOVA Information Management School, Universidade Nova de Lisboa, Lisboa, Portugal sug: subj: Statistics Probability Portugal Adolescence Female Male Alcoholism Epidemiology Scotland Adult Risk Factors Lip Neoplasms Epidemiology Young Adult Funding Source Human Adolescent: 13-18 years Adult: 19-44 years Female Male ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|