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Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends

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  • Mahmoud Torabi
  • Rhonda J. Rosychuk

Abstract

To examine childhood cancer diagnoses in the province of Alberta, Canada during 1983--2004, we construct a generalized additive mixed model for the analysis of geographic and temporal variability of cancer ratios. In this model, spatially correlated random effects and temporal components are adopted. The interaction between space and time is also accommodated. Spatio-temporal models that use conditional autoregressive smoothing across the spatial dimension and B-spline over the temporal dimension are considered. We study the patterns of incidence ratios over time and identify areas with consistently high ratio estimates as areas for potential further investigation. We apply the method of penalized quasi-likelihood to estimate the model parameters. We illustrate this approach using a yearly data set of childhood cancer diagnoses in the province of Alberta, Canada during 1983--2004.

Suggested Citation

  • Mahmoud Torabi & Rhonda J. Rosychuk, 2011. "Spatio-temporal modelling using B-spline for disease mapping: analysis of childhood cancer trends," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1769-1781, October.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1769-1781
    DOI: 10.1080/02664763.2010.529877
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    Cited by:

    1. Mahmoud Torabi, 2012. "Spatial modeling using frequentist approach for disease mapping," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(11), pages 2431-2439, July.
    2. Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1-16, February.

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