A unifying modeling framework for highly multivariate disease mapping
Abstract
Multivariate disease mapping refers to the joint mapping of multiple diseases from regionally aggregated data and continues to be the subject of considerable attention for biostatisticians and spatial epidemiologists. The key issue is to map multiple diseases accounting for any correlations among themselves. Recently, Martinez‐Beneito (2013) provided a unifying framework for multivariate disease mapping. While attractive in that it colligates a variety of existing statistical models for mapping multiple diseases, this and other existing approaches are computationally burdensome and preclude the multivariate analysis of moderate to large numbers of diseases. Here, we propose an alternative reformulation that accrues substantial computational benefits enabling the joint mapping of tens of diseases. Furthermore, the approach subsumes almost all existing classes of multivariate disease mapping models and offers substantial insight into the properties of statistical disease mapping models. Copyright © 2015 John Wiley & Sons, Ltd.
Citing Literature
Number of times cited according to CrossRef: 17
- Andrew B Lawson, NIMBLE for Bayesian Disease Mapping, Spatial and Spatio-temporal Epidemiology, 10.1016/j.sste.2020.100323, (100323), (2020).
- Mohamed Elkhouly, Marco A.R. Ferreira, Dynamic multiscale spatiotemporal models for multivariate Gaussian data, Spatial Statistics, 10.1016/j.spasta.2020.100475, (100475), (2020).
- Francisca Corpas-Burgos, Miguel A. Martinez-Beneito, On the use of adaptive spatial weight matrices from disease mapping multivariate analyses, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-020-01781-5, (2020).
- Wagner H. Bonat, Ricardo R. Petterle, Priscilla Balbinot, Alexandre Mansur, Ruth Graf, Modelling multiple outcomes in repeated measures studies: Comparing aesthetic eyelid surgery techniques, Statistical Modelling, 10.1177/1471082X20943312, (1471082X2094331), (2020).
- G. Vicente, T. Goicoa, M. D. Ugarte, Bayesian inference in multivariate spatio-temporal areal models using INLA: analysis of gender-based violence in small areas, Stochastic Environmental Research and Risk Assessment, 10.1007/s00477-020-01808-x, (2020).
- Miguel A. Martinez-Beneito, Some links between conditional and coregionalized multivariate Gaussian Markov random fields, Spatial Statistics, 10.1016/j.spasta.2019.100383, (100383), (2019).
- Pavel Chernyavskiy, Mark P Little, Philip S Rosenberg, Spatially varying age–period–cohort analysis with application to US mortality, 2002–2016, Biostatistics, 10.1093/biostatistics/kxz009, (2019).
- F. Corpas-Burgos, P. Botella-Rocamora, M. A. Martinez-Beneito, On the convenience of heteroscedasticity in highly multivariate disease mapping, TEST, 10.1007/s11749-019-00628-8, (2019).
- Ying C. MacNab, Some recent work on multivariate Gaussian Markov random fields, TEST, 10.1007/s11749-018-0605-3, 27, 3, (497-541), (2018).
- Miguel A. Martinez-Beneito, Comments on: Some recent work on multivariate Gaussian Markov random fields, TEST, 10.1007/s11749-018-0606-2, 27, 3, (542-544), (2018).
- Ying C. MacNab, Rejoinder on: Some recent work on multivariate Gaussian Markov random fields, TEST, 10.1007/s11749-018-0608-0, 27, 3, (554-569), (2018).
- Saeed Hesam, Mahmood Mahmoudi, Abbas Rahimi Foroushani, Mehdi Yaseri, Mohammad Ali Mansournia, A cause-specific hazard spatial frailty model for competing risks data, Spatial Statistics, 10.1016/j.spasta.2018.07.004, 26, (101-124), (2018).
- Harrison Quick, Lance A. Waller, Michele Casper, A multivariate space–time model for analysing county level heart disease death rates by race and sex, Journal of the Royal Statistical Society: Series C (Applied Statistics), 10.1111/rssc.12215, 67, 1, (291-304), (2017).
- Ying C. MacNab, Linear models of coregionalization for multivariate lattice data: a general framework for coregionalized multivariate CAR models, Statistics in Medicine, 10.1002/sim.6955, 35, 21, (3827-3850), (2016).
- Juste Aristide Goungounga, Jean Gaudart, Marc Colonna, Roch Giorgi, Impact of socioeconomic inequalities on geographic disparities in cancer incidence: comparison of methods for spatial disease mapping, BMC Medical Research Methodology, 10.1186/s12874-016-0228-x, 16, 1, (2016).
- Francesca Bruno, Michela Cameletti, Maria Franco-Villoria, Fedele Greco, Rosaria Ignaccolo, Luigi Ippoliti, Pasquale Valentini, Massimo Ventrucci, A survey on ecological regression for health hazard associated with air pollution, Spatial Statistics, 10.1016/j.spasta.2016.05.003, 18, (276-299), (2016).
- Ying C MacNab, Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs, Statistical Methods in Medical Research, 10.1177/0962280216660419, 25, 4, (1118-1144), (2016).
