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Activity Number: 48
Type: Invited
Date/Time: Sunday, August 9, 2015 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract #314308 View Presentation
Title: Joint Modeling of Spatial Outcomes: Benefits to Understanding the Underlying Process and Power Gains
Author(s): Charmaine Dean* and Cindy Feng and Alisha Albert-Green
Companies: University of Western Ontario and University of Saskatchewan and University of Western Ontario
Keywords: Joint disease mapping ; Conditional autoregressive model ; Markov chain Monte Carlo ; Zero-inflated Poisson ; Joint outcome modeling ; Power
Abstract:

This talk discusses joint outcome modeling of multivariate spatial data, where outcomes include count as well as zero-inflated count data. The framework utilized for the joint spatial count outcome analysis reflects that which is now commonly employed for the joint analysis of longitudinal and survival data, termed shared frailty models, in which the outcomes are linked through a shared latent spatial random risk term. We discuss these types of joint mapping models and consider the benefits achieved through such joint modeling in the disease mapping context. We also consider the power of tests for common spatial structure and develop recommendations on the sort of power achievable in some contexts, as well as overall recommendations on the utility of joint mapping. We illustrate the approaches in an analysis of lung cancer mortality as well as an ecological study of Comandra blister rust infection of lodgepole pine trees.


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