1. Disease mapping via negative binomial regression M-quantiles.
- Author
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Chambers R, Dreassi E, and Salvati N
- Subjects
- Computer Simulation, England, Epidemiologic Methods, Humans, Infant, Low Birth Weight, Infant, Newborn, Lip Neoplasms epidemiology, Monte Carlo Method, Risk Factors, Scotland epidemiology, Binomial Distribution, Geographic Mapping, Regression Analysis, Spatial Analysis
- 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., (Copyright © 2014 John Wiley & Sons, Ltd.)
- Published
- 2014
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