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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21026

Title: Bayesian multi-scale modeling for aggregated disease mapping data
Authors: Aregay, Mehreteab
Lawson, A.B.
Faes, C.
Kirby, R.S.
Issue Date: 2017
Citation: Statistical methods in medical research 26(6), p. 2726-2742
Abstract: In disease mapping, a scale effect due to an aggregation of data from a finer resolution level 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 level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria.
URI: http://hdl.handle.net/1942/21026
DOI: 10.1177/0962280215607546
ISI #: 000418307900016
ISSN: 0962-2802
Category: A1
Type: Journal Contribution
Validation: vabb, 2017
Appears in Collections: Research publications

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