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Activity Number: 116
Type: Topic Contributed
Date/Time: Monday, August 10, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #315067 View Presentation
Title: Bayesian Multiscale Modeling for Aggregated Disease Mapping Data
Author(s): Mehreteab Aregay* and Andrew B. Lawson and Christel Faes and Russel S. Kirby
Companies: Medical University of South Carolina and Medical University of South Carolina and Hasselt University and University of South Florida
Keywords: Deviance information criterion (DIC) ; Watanabe-Akaike or widely applicable information criterion (WAIC) ; predictive accuracy ; shared random effect model ; scaling effect
Abstract:

In disease mapping, a scaling effect due to an aggregation of data from a finer to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer levels. All these models were compared based on predictive accuracy, deviance information criterion (DIC), and Watanabe-Akaike or widely applicable information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effect outperform the other models on a range of criteria.


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