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Disease mapping via negative binomial regression M-quantiles

Journal Article


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.

Authors


  •   Chambers, Raymond L.
  •   Dreassi, Emanuela (external author)
  •   Salvati, Nicola (external author)

Publication Date


  • 2014

Citation


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

Scopus Eid


  • 2-s2.0-84939271674

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3467

Number Of Pages


  • 19

Start Page


  • 4805

End Page


  • 4824

Volume


  • 33

Issue


  • 27

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.

Authors


  •   Chambers, Raymond L.
  •   Dreassi, Emanuela (external author)
  •   Salvati, Nicola (external author)

Publication Date


  • 2014

Citation


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

Scopus Eid


  • 2-s2.0-84939271674

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3467

Number Of Pages


  • 19

Start Page


  • 4805

End Page


  • 4824

Volume


  • 33

Issue


  • 27