Bayesian joint spatio-temporal analysis of multiple diseases

  1. Virgilio Gómez-Rubio
  2. Francisco Palmí-Perales
  3. Gonzalo López-Abente
  4. Rebeca Ramis-Prieto
  5. Pablo Fernández-Navarro
Revue:
Sort: Statistics and Operations Research Transactions

ISSN: 1696-2281

Année de publication: 2019

Volumen: 43

Número: 1

Pages: 51-74

Type: Article

DOI: 10.2436/20.8080.02.79 DIALNET GOOGLE SCHOLAR lock_openAccès ouvert editor

D'autres publications dans: Sort: Statistics and Operations Research Transactions

Résumé

In this paper we propose a Bayesian hierarchical spatio-temporal model for the joint analysis of multiple diseases which includes specific and shared spatial and temporal effects. Dependence on shared terms is controlled by disease-specific weights so that their posterior distribution can be used to identify diseases with similar spatial and temporal patterns. The model proposed here has been used to study three different causes of death (oral cavity, esophagus and stomach cancer) in Spain at the province level. Shared and specific spatial and temporal effects have been estimated and mapped in order to study similarities and differences among these causes. Furthermore, estimates using Markov chain Monte Carlo and the integrated nested Laplace approximation are compared.

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This work has been supported by grants PPIC-2014-001-P and SBPLY/17/180501/ 000491, funded by Consejería de Educación, Cultura y Deportes (Castilla-La Man-cha, Spain) and Fondo Europeo de Desarrollo Regional, and grant MTM2016-77501-P, funded by the Ministerio de Economía y Competitividad (Spain). F. Palmí-Perales was supported by a doctoral scholarship awarded by the University of Castilla-La Mancha (Spain). We also thank Prof. Håvard Rue for his help with the implementation of the model using INLA.

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Références bibliographiques

  • Abellan, J., Richardson, S. and Best, N. (2008). Use of space-time to investigate the stability of patterns of disease. Enviromental Health Perspectives, 116, 1111–1119.
  • Aragonés, N., Pérez-Gómez, B., Pollán, M., Ramis, R., Vidal, E., Lope, V., Garcı́a-Pérez, J., Boldo, E. and López-Abente, G. (2009). The striking geographical pattern of gastric cancer mortality in Spain: environmental hypotheses revisited. BMC cancer, 9, 1.
  • Aragonés, N., Ramis, R., Pollán, M., Pérez-Gómez, B., Gómez-Barroso, D., Lope, V., Boldo, E., Garcı́aPérez, J. and López-Abente, G. (2007). Oesophageal cancer mortality in Spain: a spatial analysis. BMC cancer,7, 1.
  • Banerjee, S., Carlin, B. and Gelfand, A. (2014). Hierarchical Modeling and Analysis for Spatial Sata. Crc Press.
  • Besag, J., York, J. and Mollié, A. (1991). Bayesian image restoration, with two applications in spatial statistics. Annals of the Institute of Statistical Mathematics, 43, 1–59.
  • Botella-Rocamora, P., Martı́nez-Beneito, M. and Banerjee, S. (2015). A unifying modeling framework for highly multivariate disease mapping. Statistics in Medicine, 34, 1548–1559.
  • Carroll, R., Lawson, A. B., Faes, C., Kirby, R. S., Aregay, M. and Watjou, K. (2016). Spatio-temporal bayesian model selection for disease mapping. Environmetrics, 27, 466–478.
  • Carroll, R., Lawson, A. B., Faes, C., Kirby, R. S., Aregay, M. and Watjou, K. (2017). Extensions to multivariate space time mixture modeling of small area cancer data. International Journal of Environmental Research and Public Health, 14, 503.
  • Carroll, R., Lawson, A. B., Kirby, R. S., Faes, C., Aregay, M. and Watjou, K. (2017). Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation. Annals of Epidemiology, 27, 42–51.
  • Corberán-Vallet, A. (2012). Prospective surveillance of multivariate spatial disease data. Statistical Methods in Medical Research, 21, 457–477.
  • Downing, A., Forman, D., Gilthorpe, M., Edwards, K. and Manda, S. (2008). Joint disease mapping using six cancers in the Yorkshire region of England. International Journal of Health Geographics, 7, 1.
  • Elliot, P., Wakefield, J., Best, N. and Briggs, D. (2000). Spatial Epidemiology: Methods and Applications. Oxford University Press.
  • Ferlay, J., Shin, H., Bray, F., Forman, D., Mathers, C. and Parkin, D. (2012). Cancer incidence and mortality worldwide: Iarc cancerbase no. 10 [internet]. international agency for research on cancer, Lyon, France. globocan 2008 v1. 2.
  • Gelfand, A. and Vounatsou, P. (2003). Proper multivariate conditional autoregressive models for spatial data analysis. Biostatistics, 4, 11–15.
  • Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis, 1, 515–534.
  • Gilks, W., Richardson, S. and Spiegelhalter, D. (1996). Markov Chain Monte Carlo in Practice. Boca Raton, Florida: Chapman & Hall.
  • Goicoa, T., Adin, A., Ugarte, M. D. and Hodges, J. S. (2018). In spatio-temporal disease mapping models, identifiability constraints affect pql and inla results. Stochastic Environmental Research and Risk Assessment, 32, 749–770.
  • Guangquan, L., Best, N., Hansell, A., Ahmed, I. and Richardson, S. (2012). Baystdetect: detecting unusual temporal patterns in small area data via bayesian model choice. Biostatistics, 13, 695–710.
  • Jin, X., Carlin, B. and Banerjee, S. (2005). Generalized hierarchical multivariate car models for areal data. Biometrics, 61, 950–961.
  • Knorr-Held, L. (2000). Bayesian modelling of inseparable space-time variation in disease risk. Statistics in Medicine, 19, 2555–2567.
  • Knorr-Held, L. and Best, N. (2001). A shared component model for detecting joint and selective clustering of two diseases. Journal of the Royal Statistical Society, Series A, 1, 73–85.
  • Lawson, A. (2013). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology. CRC press.
  • Lawson, A. B., Carroll, R., Faes, C., Kirby, R. S., Aregay, M. and Watjou, K. (2017). Spatiotemporal multivariate mixture models for bayesian model selection in disease mapping. Environmetrics, 28, e2465.
  • López-Abente, G., Aragonés, N., Garcı́a-Pérez, J. and Fernández-Navarro, P. (2014). Disease mapping and spatio-temporal analysis: importance of expected-case computation criteria. Geospatial Health, 9, 27–35.
  • López-Abente, G., Aragonés, N., Pérez-Gómez, B., Pollán, M., Garcı́a-Pérez, J., Ramis, R. and FernándezNavarro, P. (2014). Time trends in municipal distribution patterns of cancer mortality in Spain. BMC cancer, 14, 1.
  • López-Abente, G., Ramis, R., Pollán, M., Aragonés, N., Pérez-Gómez, B., Gómez-Barroso, D., Carrasco,
  • J., Lope, V., Garcı́a-Pérez, J., Boldo, E. and Garcı́a-Mendizabal, M. (2007). Atlas Municipal de Mortalidad por Cáncer en España, 1989-1998. Madrid: Instituto de Salud Carlos III.
  • Lunn, D., Thomas, A., Best, N. and Spiegelhalter, D. (2000). WinBUGS – a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing, 10, 325–337.
  • MacNab, Y. (2011). On gaussian markov random fields and bayesian disease mapping. Statistical Methods in Medical Research, 20, 49–68.
  • Mardia, K. (1988). Multi-dimensional multivariate gaussian markov random fields with application to image processing. Journal of Multivariate Analysis, 24, 265–284.
  • Marı́-Dell’Olmo, M., Martı́nez-Beneito, M., Gotsens, M. and Palència, L. (2014). A smoothed ANOVA model for multivariate ecological regression. Stochastic Environmental Research and Risk Assessment, 28, 695–706.
  • Martı́nez-Beneito, M. (2013). A general modelling framework for multivarite disease mapping. Biometrika, 100, 539–553.
  • Martı́nez-Beneito, M., Botella-Rocamora, P. and Banerjee, S. (2016). Towards a multidimensional approach to bayesian disease mapping. Bayesian Analysis, 1, 239–259.
  • R Core Team (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria,: R Foundation for Statistical Computing.
  • Richardson, S., Abellan, J. J. and Best, N. (2006). Bayesian spatio-temporal analysis of joint patterns of male and female lung cancer risks in Yorkshire (UK). Statistical Methods in Medical Research, 15, 385–407. PMID: 16886738.
  • Rue, H. and Held, L. (2005). Gaussian Markov Random Fields: Theory and Applications. CRC Press.
  • Rue, H., Martino, S. and Chopin, N. (2009). Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society, Series B, 71 (Part 2), 319–392.
  • Seoane-Mato, D., Aragonés,N., Ferreras, E., Garcı́a-Pérez, J., Cervantes-Amat, M., Fernández-Navarro, P., Pastor-Barriuso, R. and López-Abente, G. (2014). Trends in oral cavity, pharyngeal, oesophageal and gastric cancer mortality rates in Spain, 1952–2006: an age-period-cohort analysis. BMC cancer, 14, 1.
  • Sturtz, S., Ligges, U. and Gelman, A. (2005). R2WinBUGS: A package for running winbugs from R. Journal of Statistical Software, 12, 1–16.
  • Ugarte, M. D., Adin, A. and Goicoa, T. (2016). Two-level spatially structured models in spatio-temporal disease mapping. Statistical Methods in Medical Research, 25, 1080–1100. PMID: 27566767.
  • Wang, F. and Wall, M. (2003). Generalized common spatial factor model. Biostatistics, 4, 569–582.
  • Zhang, Y., Hodges, J. and Banerjee, S. (2009). Smoothed anova with spatial effects as a competitor to mcar in multivariate spatial smoothing. The Annals of Applied Statistics, 3, 1805.
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