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Aregay M, Lawson AB, Faes C, et al. Spatial mixture multiscale modeling for aggregated health data. Biom J. 2016;58(5):1091-112doi: 10.1002/bimj.201500168.
Aregay, M., Lawson, A. B., Faes, C., Kirby, R. S., Carroll, R., & Watjou, K. (2016). Spatial mixture multiscale modeling for aggregated health data. Biometrical journal. Biometrische Zeitschrift, 58(5), 1091-112. https://doi.org/10.1002/bimj.201500168
Aregay, Mehreteab, et al. "Spatial mixture multiscale modeling for aggregated health data." Biometrical journal. Biometrische Zeitschrift vol. 58,5 (2016): 1091-112. doi: https://doi.org/10.1002/bimj.201500168
Aregay M, Lawson AB, Faes C, Kirby RS, Carroll R, Watjou K. Spatial mixture multiscale modeling for aggregated health data. Biom J. 2016 Sep;58(5):1091-112. doi: 10.1002/bimj.201500168. Epub 2016 Feb 29. PMID: 26923178.
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Biom J. 2016 Sep;58(5):1091-112. doi: 10.1002/bimj.201500168. Epub 2016 Feb 29.

Spatial mixture multiscale modeling for aggregated health data.

Biometrical journal. Biometrische Zeitschrift

Mehreteab Aregay, Andrew B Lawson, Christel Faes, Russell S Kirby, Rachel Carroll, Kevin Watjou

Affiliations

  1. Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, MUSC, 135 Cannon Street Suite 303, MSC 835, Charleston, SC, 29425-8350, USA. [email protected].
  2. Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, MUSC, 135 Cannon Street Suite 303, MSC 835, Charleston, SC, 29425-8350, USA.
  3. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Martelarenlaan 42, Hasselt, BE3500, Belgium.
  4. Department of Community and Family Health, University of South Florida, 13201 Bruce B. Downs Blvd, MDC 56, Tampa, FL, 33612, USA.

PMID: 26923178 DOI: 10.1002/bimj.201500168

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.

© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Keywords: Correlated heterogeneity (CH); Multiscale models; Scaling effect; Spatial mixture model; Uncorrelated heterogeneity (UH)

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