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.
|Autori:||Chambers R.;Dreassi E.; Salvati N.|
|Titolo:||Disease Mapping via Negative Binomial Regression M- quantiles|
|Anno del prodotto:||2014|
|Digital Object Identifier (DOI):||10.1002/sim.6256|
|Appare nelle tipologie:||1.1 Articolo in rivista|