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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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