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Among the proposals for joint disease mapping, the shared component model hasbecome more popular. Another recent advance to strengthen inference of diseasedata has been the extension of purely spatial models to include time andspace-time interaction. Such analyses have additional benefits over purelyspatial models. However, only a few proposed spatio-temporal models couldaddress analysing multiple diseases jointly.  In the proposed model, each component is shared by different subsets ofdiseases, spatial and temporal trends are considered for each component, andthe relative weight of these trends for each component for each relevantdisease can be estimated. We present an application of the proposed method onincidence rates of seven prevalent cancers in Iran. The effect of the sharedcomponents on the individual cancer types can be identified. Regional andtemporal variation in relative risks is shown. We present a model whichcombines the benefits of shared-components with spatio-temporal techniques formultivariate data. We show, how the model allows to analyse geographical andtemporal variation among diseases beyond previous approaches.

A Spatio-Temporal Multivariate Shared Component Model with an Application in Iran Cancer Data