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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/22823

Title: Spatial mixture multiscale modeling for aggregated health data
Authors: Aregay, Mehreteab
Lawson, Andrew B.
Faes, Christel
Kirby, Russell S.
Carroll, Rachel
Watjou, Kevin
Issue Date: 2016
Citation: BIOMETRICAL JOURNAL, 58(5), p. 1091-1112
Abstract: One of the main goals in spatial epidemiology is to study the geographical pattern of disease risks. For such purpose, the convolution model composed of correlated and uncorrelated components is often used. However, one of the two components could be predominant in some regions. To investigate the predominance of the correlated or uncorrelated component for multiple scale data, we propose four different spatial mixture multiscale models by mixing spatially varying probability weights of correlated (CH) and uncorrelated heterogeneities (UH). The first model assumes that there is no linkage between the different scales and, hence, we consider independent mixture convolution models at each scale. The second model introduces linkage between finer and coarser scales via a shared uncorrelated component of the mixture convolution model. The third model is similar to the second model but the linkage between the scales is introduced through the correlated component. Finally, the fourth model accommodates for a scale effect by sharing both CH and UH simultaneously. We applied these models to real and simulated data, and found that the fourth model is the best model followed by the second model.
Notes: [Aregay, Mehreteab; Lawson, Andrew B.; Carroll, Rachel] MUSC, Dept Publ Hlth Sci, Div Biostat & Bioinformat, 135 Cannon St Suite 303,MSC 835, Charleston, SC 29425 USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Martelarenlaan 42, BE-3500 Hasselt, Belgium. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, 13201 Bruce B Downs Blvd,MDC 56, Tampa, FL 33612 USA.
URI: http://hdl.handle.net/1942/22823
DOI: 10.1002/bimj.201500168
ISI #: 000383687100006
ISSN: 0323-3847
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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