Abstract
Two models for jointly analysing the spatial variation of incidences of three (or more) diseases, with common and uncommon risk factors, are compared via a simulation experiment. In both models, the linear predictor can be decomposed into shared and disease-specific spatial variability components. The two models are the shared model on the original formulation that use exchangeable Poisson distribution as response multivariate variable and shared components model that use a Multinomial one. The simulation study, performed using three different degree of spatial unstructured poisson over-dispersion, shows that models behave similarly. However, they perform differently for the shared clustering terms when a different level of spatial unstructured over-dispersion is present.
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References
Bernardinelli, L., Pascutto, C., Best, N. G., & Gilks, W. R. (1997). Disease mapping with errors in covariates. Statistics in Medicine,16, 741–752.
Besag, J., York, J., & Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics,43, 1–59.
Dabney, A. R., & Wakefield, J. C. (2005). Issues in the mapping of two diseases. Statistical Methods in Medical Research,14, 83–112.
Dreassi, E. (2007). Polytomous disease mapping to detect uncommon risk factors for related diseases. Biometrical Journal,49(4), 520–529.
Gelfand, A., & Vounatsou, P. (2003). Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics,4, 11–25.
Held, L., Natário, I., Fenton, S. E., Rue, H., & Becker, N. (2005). Towards joint disease mapping. Statistical Methods in Medical Research,14, 61–82.
Jin, X., Carlin B. P., & Banerjee, S. (2005). Generalized hierarchical multivariate CAR models for areal data. Biometrics,61(4), 950–961.
Knorr-Held, L., & Best, N. (2001). A shared component model for detecting joint and selective clustering of two diseases. Journal of the Royal Statistical Society Series A (Statistics in Society),164, 73–86.
Langford, I. H., Leyland, A. H., Rasbash, J., & Goldstein, H. (1999). Multilevel modelling of the geographical distributions of diseases. Journal of the Royal Statistical Society Series C (Applied Statistics),48, 253–268.
Leyland, A. H., Langford, I. H., Rasbash, J., & Goldstein, H. (2000). Multivariate spatial models for event data. Statistics in Medicine,19(17–18), 2469–2478.
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Dreassi, E. (2013). Shared Components Models in Joint Disease Mapping: A Comparison. In: Giusti, A., Ritter, G., Vichi, M. (eds) Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28894-4_25
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DOI: https://doi.org/10.1007/978-3-642-28894-4_25
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