A flexible random effects distribution in disease mapping models
Oscar Owino Ngesa, Henry Godwell Mwambi, Thomas Achia · South African Statistical Journal · 2014
Disease mapping has seen many applications in epidemiology and public health. The basic model used in disease mapping is the Besag, York and Mollie model, which incorporates two random effects, one which is spatially structured and the other random effect which is spatially unstructured. The normality assumption on the spatially unstructured random effect is very common. In this work, we investigate a more robust spatially unstructured random effect distribution by considering the symmetric generalized Gaussian distribution in the disease mapping problem. The distribution has the normal and Laplace distributions as special cases. The inferences under this model are carried out under the Bayesian approach implemented in WinBUGS. The generalized Gaussian distribution is introduced in WinBUGS using zero tricks. The usefulness of the proposed model is investigated with a simulation study and applied in real data; mapping tuberculosis in Kenya. In this paper we showed that the generalized Gaussian distribution can produce better results when the normality assumption is violated due to high peakedness or less peakedness in the data. For the case of data in which the random effects are truly normal, the generalized Gaussian distribution adjusts to a normal distribution as dictated by the data itself.