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Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data

Author

Listed:
  • Rachel Carroll

    (Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA)

  • Andrew B. Lawson

    (Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA)

  • Christel Faes

    (Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, 3500 Hasselt, Belgium)

  • Russell S. Kirby

    (Department of Community and Family Health, University of South Florida, Tampa, FL 33620, USA)

  • Mehreteab Aregay

    (Department of Public Health Sciences, Medical University of South Carolina, 135 Cannon St, Charleston, SC 29425, USA)

  • Kevin Watjou

    (Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, 3500 Hasselt, Belgium)

Abstract

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

Suggested Citation

  • Rachel Carroll & Andrew B. Lawson & Christel Faes & Russell S. Kirby & Mehreteab Aregay & Kevin Watjou, 2017. "Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data," IJERPH, MDPI, vol. 14(5), pages 1-13, May.
    • Handle: RePEc:gam:jijerp:v:14:y:2017:i:5:p:503-:d:98002
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    References listed on IDEAS

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    1. Leonhard Knorr‐Held & Nicola G. Best, 2001. "A shared component model for detecting joint and selective clustering of two diseases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 73-85.
    2. Rachel Carroll & Andrew B. Lawson & Christel Faes & Russell S. Kirby & Mehreteab Aregay & Kevin Watjou, 2016. "Spatio‐temporal Bayesian model selection for disease mapping," Environmetrics, John Wiley & Sons, Ltd., vol. 27(8), pages 466-478, December.
    3. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    4. Andrew B. Lawson & Rachel Carroll & Christel Faes & Russell S. Kirby & Mehreteab Aregay & Kevin Watjou, 2017. "Spatiotemporal multivariate mixture models for Bayesian model selection in disease mapping," Environmetrics, John Wiley & Sons, Ltd., vol. 28(8), December.
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    Cited by:

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    2. Samuel O. M Manda & Nada Abdelatif, 2017. "Smoothed Temporal Atlases of Age-Gender All-Cause Mortality in South Africa," IJERPH, MDPI, vol. 14(9), pages 1-18, September.

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