Comparing CAR and P-spline models in spatial disease mapping
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Smoothing risks is one of the main goals in disease mapping as classical measures, such as standardized mortality ratios, can be extremely variable. However, smoothing risks might hinder the detection of high risk areas, since these two objectives are somewhat contradictory. Most of the work on smoothing risks and detection of high risk areas has been derived using conditional autoregressive (CAR) models. In this work, penalized splines (P-splines) models are also investigated. Confidence intervals for the log-relative risk predictor will be derived as a tool to detect high-risk areas. The performance of P-spline and CAR models will be compared in terms of smoothing (relative bias), sensitivity (ability to detect high risk areas), and specificity (ability to discard false patterns created by noise) through a simulation study based on the well-known Scottish lip cancer data.
KeywordsPRIDE model Smoothing risks Simulation study MSE PQL Lip cancer
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- Besag J (1974) Spatial interaction and the statistical analysis of lattice systems (with discussions). J R Stat Soc Ser B 36: 192–236Google Scholar
- Lawson AB (2009) Bayesian disease mapping. Hierarchical modelling in spatial epidemiology. Chapman and Hall, Boca RatonGoogle Scholar
- Leroux BG, Lei X, Breslow N (1999) Estimation of disease rates in small areas: a new mixed model for spatial dependence. In: Halloran ME, Berry D (eds) Statistical models in epidemiology, the environment and clinical trials. Springer, New York, NY, pp 179–192Google Scholar
- R Development Core Team (2010) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.r-project.org/
- Spiegelhalter DJ, Thomas A, Best NG, Lunn DJ (2003) WinBUGS version 1.4 user’s manual. MRC Biostatiscs unit. Institute of Public Health, Cambridge; Rolf Nevanlinna Institute, University of Helsinki; and Department of Epidemiology and Public Health, Imperial College London. http://www.mrc-bsu.cam.ac.uk/bugs