Space-time interactions in Bayesian disease mapping with recent tools: making things easier for practitioners

This repository contains the R code to fit the models described in the paper entitled "Space-time interactions in Bayesian disease mapping with recent tools: making things easier for practitioners" (Urdangarin et al., 2022)

Table of contents

• Data
• R code
• References

Data

Female breast cancer mortality data (ICD-10 code 50) in Spanish provinces during the period 1990-2010.

• BreastCancer_data.Rdata

  This .Rdata contains the following objects

  • Data: data.frame object with the number of observed and expected cases ('Counts' and 'Expected' variables, respectively) for each province ('Area') and time period ('Year') for female breast cancer mortality data.
  • Carto_ESP: sf object containing the spatial polygons of the Spanish provinces. The data contains a data.frame with 50 rows and 'Area' (character vector of geographic identifiers), 'Name' (character vector of province names), 'Longitude' (numeric vector of longitude values), 'Latitude' (numeric vector of latitude values) and 'geometry' (sfc_MULTIPOLYGON) variables.
  • Rs: adjacency matrix.

R code

R code to fit the spatio-temporal models described in the paper has been included here. Only models for the set of hyperprior distributions H1 are shown (to fit the models with H2 and H3 hyperprior distributions slight modifications are required in the code).

• icar_models and bym_models folders contain the Rscripts with the spatio-temporal models fitted with ICAR and BYM spatial priors using R-INLA, Nimble 1 and Nimble 2.
• run folder contains the Rscripts to run these models.
• tables_figures_paper.R contains the necessary functions to reproduce all the figures and tables of Spanish breast cancer mortality data analysis.

Computations were run using R-4.0.3, INLA version 21.02.23 and NIMBLE version 0.11.1.

Acknowledgements

This work has been supported by Project PID2020-113125RB-I00/ MCIN/ AEI/ 10.13039/501100011033.

References

Urdangarin, A., Goicoa, T. and Ugarte, M.D. (2022). Space-time interactions in Bayesian disease mapping with recent tools: making things easier for practitioners. _Statistical Methods in Medical Research, vol 31 (6), pp 1085-1103.DOI: 10.1177/09622802221079351