Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation

作者：Carroll Rachel^*; Lawson Andrew B; Kirby Russell S; Faes Christel; Aregay Mehreteab; Watjou Kevin

来源：Annals of Epidemiology, 2017, 27(1): 42-51.

DOI：10.1016/j.annepidem.2016.08.014

• Spatiotemporal
• Bayesian
• Multivariate
• Respiratory cancers
• Mixture modeling
• MCMC
• Lung and bronchus 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.

• 出版日期2017-1