Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24335
Title: Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data
Authors: Carroll, Rachel
LAWSON, Andrew 
FAES, Christel 
Kirby, Russell S.
AREGAY, Mehreteab 
WATJOU, Kevin 
Issue Date: 2017
Publisher: MDPI AG
Source: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 14(5), p. 1-13 (Art N° 503)
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.
Notes: [Carroll, Rachel; Lawson, Andrew B.; Aregay, Mehreteab] Med Univ South Carolina, Dept Publ Hlth Sci, 135 Cannon St, Charleston, SC 29425 USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, B-3500 Hasselt, Belgium. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL 33620 USA.
Keywords: lung and bronchus cancer; melanoma cancer of the skin; oral cavity and pharynx cancer; incidence; mixture model; spatio-temporal; disease mapping;lung and bronchus cancer; melanoma cancer of the skin; oral cavity and pharynx cancer; incidence; mixture model; spatio-temporal; disease mapping
Document URI: http://hdl.handle.net/1942/24335
ISSN: 1661-7827
e-ISSN: 1660-4601
DOI: 10.3390/ijerph14050503
ISI #: 000404106400051
Rights: © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Category: A1
Type: Journal Contribution
Validations: ecoom 2018
Appears in Collections:Research publications

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