Rachel Carroll1, Andrew B Lawson2, Russell S Kirby3, Christel Faes4, Mehreteab Aregay2, Kevin Watjou4. 1. Department of Public Health, Medical University of South Carolina, Charleston. Electronic address: mosra@musc.edu. 2. Department of Public Health, Medical University of South Carolina, Charleston. 3. Department of Community and Family Health, University of South Florida, Tampa. 4. Interuniversity Institute for Statistics and Statistical Bioinformatics, Hasselt University, Agoralaan 1, Diepenbeek, Belgium.
Abstract
PURPOSE: Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. METHODS: In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. RESULTS: Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. CONCLUSIONS: Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer.
PURPOSE: Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. METHODS: In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. RESULTS: Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. CONCLUSIONS: Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer.
Authors: Rachel Carroll; Andrew B Lawson; Christel Faes; Russell S Kirby; Mehreteab Aregay; Kevin Watjou Journal: Int J Environ Res Public Health Date: 2017-05-09 Impact factor: 3.390
Authors: Farzana Jahan; Earl W Duncan; Susana M Cramb; Peter D Baade; Kerrie L Mengersen Journal: Int J Health Geogr Date: 2020-10-17 Impact factor: 3.918