Shared Components Models in Joint Disease Mapping: A Comparison

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 (named shared clustering and specific clustering respectively). 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 shows that models behave similarly. However, Multinomial shared components model performs better for disease-specific spatial variability clustering terms but it is lower for the shared one.