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|Title: ||Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation|
|Authors: ||Carroll, Rachel|
Lawson, Andrew B.
Kirby, Russell S.
|Issue Date: ||2017|
|Publisher: ||ELSEVIER SCIENCE INC|
|Citation: ||ANNALS OF EPIDEMIOLOGY, 27(1), p. 42-51|
|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. (C) 2016 Elsevier Inc. All rights reserved.|
|Notes: ||[Carroll, Rachel; Lawson, Andrew B.; Aregay, Mehreteab] Med Univ South Carolina, Dept Publ Hlth, Charleston, SC 29425 USA. [Kirby, Russell S.] Univ S Florida, Dept Community & Family Hlth, Tampa, FL USA. [Faes, Christel; Watjou, Kevin] Hasselt Univ, Interuniv Inst Stat & Stat Bioinformat, Agoralaan 1, Diepenbeek, Belgium.|
|ISI #: ||000393268100006|
|Type: ||Journal Contribution|
|Appears in Collections: ||Research publications|
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