Highly multivariate disease mapping has recently been proposed as an enhancement of traditional multivariate studies, making it possible to perform the joint analysis of a large number of diseases. This line of research has an important potential since it integrates the information of many diseases into a single model yielding richer and more accurate risk maps. In this paper we show how some of the proposals already put forward in this area display some particular problems when applied to small regions of study. Specifically, the homoscedasticity of these proposals may produce evident misfits and distorted risk maps. In this paper we propose two new models to deal with the variance-adaptivity problem in multivariate disease mapping studies and give some theoretical insights on their interpretation.
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The authors acknowledge the support of the research Grant PI16/01004 (co-funded with FEDER grants) of Instituto de Salud Carlos III and predoctoral contract UGP-15-156 of FISABIO.
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Corpas-Burgos, F., Botella-Rocamora, P. & Martinez-Beneito, M.A. On the convenience of heteroscedasticity in highly multivariate disease mapping. TEST 28, 1229–1250 (2019). https://doi.org/10.1007/s11749-019-00628-8
- Gaussian Markov random fields
- Multivariate disease mapping
- Bayesian statistics
- Spatial statistics
- Mortality studies
Mathematics Subject Classification
- 62P10-Applications to biology and medical sciences