Disease mapping via negative binomial regression M-quantiles
Copyright policy )Disease mapping via negative binomial regression M-quantiles
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 and a random effects modelling approach for disease mapping. This suggests that the M-quantile approach leads to predicted relative risks with smaller root mean square error than standard disease mapping methods. 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.
23 pages, 7 figures
- University of Florence Italy
- University of Pisa Italy
- University of Wollongong Australia
- New York University Italy
via, FOS: Computer and information sciences, ecological regression; overdispersed count data; robust models; spatial correlation, Geographic Mapping, m, Science and Technology Studies, 310, binomial, Methodology (stat.ME), Engineering, Risk Factors, Humans, Computer Simulation, mapping, Statistics - Methodology, disease, Spatial Analysis, negative, Infant, Newborn, Infant, Low Birth Weight, Binomial Distribution, England, Scotland, Lip Neoplasms, Regression Analysis, regression, quantiles, Epidemiologic Methods, Monte Carlo Method
via, FOS: Computer and information sciences, ecological regression; overdispersed count data; robust models; spatial correlation, Geographic Mapping, m, Science and Technology Studies, 310, binomial, Methodology (stat.ME), Engineering, Risk Factors, Humans, Computer Simulation, mapping, Statistics - Methodology, disease, Spatial Analysis, negative, Infant, Newborn, Infant, Low Birth Weight, Binomial Distribution, England, Scotland, Lip Neoplasms, Regression Analysis, regression, quantiles, Epidemiologic Methods, Monte Carlo Method
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