Joint space-time Bayesian disease mapping via quantification of disease risk association.

Abstract

Alzheimer's disease is an increasingly prevalent neurological disorder with no effective therapies. Thus, there is a need to characterize the progression of Alzheimer's disease risk in order to preclude its inception in patients. Characterizing Alzheimer's disease risk can be accomplished at the population-level by the space-time modeling of Alzheimer's disease incidence data. In this paper, we develop flexible Bayesian hierarchical models which can borrow risk information from conditions antecedent to Alzheimer's disease, such as mild cognitive impairment, in an effort to better characterize Alzheimer's disease risk over space and time. From an application of these models to real-world Alzheimer's disease and mild cognitive impairment spatiotemporal incidence data, we found that our novel models provided improved model goodness of fit, and via a simulation study, we demonstrated the importance of diagnosing the label-switching problem for our models as well as the importance of model specification in order to best capture the contribution of time in modeling Alzheimer's disease risk.