Title

Disease mapping via negative binomial regression M-quantiles

RIS ID

91786

Publication Details

Chambers, R. L., Dreassi, E. & Salvati, N. (2014). Disease mapping via negative binomial regression M-quantiles. Statistics in Medicine, 33 (27), 4805-4824.

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. 2014 John Wiley & Sons, Ltd.

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Link to publisher version (DOI)

http://dx.doi.org/10.1002/sim.6256