title: Disease mapping via negative binomial regression M-quantiles.
abstract: We introduce a semi-parametric approach to ecological regression for disease mapping, based on modelling the regression M-quantiles of a negative binomial variable. The proposed method is robust to outliers in the model covariates, including those due to measurement error, and can account for both spatial heterogeneity and spatial clustering. A simulation experiment based on the well-known Scottish lip cancer data set is used to compare the M-quantile modelling approach with a disease mapping approach based on a random effects model. This suggests that the M-quantile approach leads to predicted relative risks with smaller root mean square error. The paper concludes with an illustrative application of the M-quantile approach, mapping low birth weight incidence data for English Local Authority Districts for the years 2005-2010.
authors.affiliation: National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, Australia.
authors.forename: Ray
authors.initials: R
authors.lastname: Chambers
authors.forename: Emanuela
authors.initials: E
authors.lastname: Dreassi
authors.forename: Nicola
authors.initials: N
authors.lastname: Salvati
mesh_terms: D016010:Binomial Distribution; D003198:Computer Simulation; D004739:England; D004812:Epidemiologic Methods; D062305:Geographic Mapping; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D008048:Lip Neoplasms / Q000453:epidemiology; D009010:Monte Carlo Method; D012044:Regression Analysis; D012307:Risk Factors; D012606:Scotland / Q000453:epidemiology; D062206:Spatial Analysis
keywords: ecological regression; overdispersed count data; robust models; spatial correlation