Autor(es): Rampaso, Renato Couto [UNESP] ; Pires de Souza, Aparecida Doniseti [UNESP] ; Flores, Edilson Ferreira [UNESP]
Data: 2018
Identificador Persistente: http://hdl.handle.net/11449/164959
Origem: Oasisbr
Assunto(s): conditional autoregressive models; disease mapping; spatial Bayesian inference; conditional autoregressive models; conditional autoregressive models; disease mapping; disease mapping; spatial Bayesian inference; spatial Bayesian inference
Made available in DSpace on 2018-11-27T04:40:24Z (GMT). No. of bitstreams: 0 Previous issue date: 2016-02-11
In disease mapping, the overall goal is to study the incidence or mortality risk caused by a specific disease in a number of geographical regions. It is common to assume that the response variable follows a Poisson distribution, whose average rate can be explained by a group of covariates and a random effect. For this random effect, it is considered conditional autoregressive (CAR) models, which carry information about the neighbourhood relationship between the regions. The focus of this paper was to explore and compare some CAR models proposed in the literature. An application with epidemiological data was conducted to model the risk of death due to Crohn's Disease and Ulcerative Colitis in the State of SAo Paulo - Brazil. Finally, a simulation study was done to strengthen the results and assess the performance of the models in the presence of various levels of spatial dependence.
Univ Estadual Paulista, Fac Ciencias & Tecnol, Presidente Prudente, SP, Brazil
Univ Estadual Paulista, Fac Ciencias & Tecnol, Presidente Prudente, SP, Brazil
