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

详细信息    查看全文

• 作者：Rachel Carroll ; PhD^a ; ^{mosra@musc.edu} ; Andrew B. Lawson ; PhD^a ; Russell S. Kirby ; PhD^b ; Christel Faes ; PhD^c ; Mehreteab Aregay ; PhD^a ; Kevin Watjou ; MS^c
• 关键词：Spatiotemporal ; Bayesian ; Multivariate ; Respiratory cancers ; Mixture modeling ; MCMC ; Lung and bronchus cancer
• 刊名：Annals of Epidemiology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：27
• 期：1
• 页码：42-51
• 全文大小：2583 K
• 卷排序：27

文摘

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.MethodsIn 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.ResultsOf 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.ConclusionsSpatiotemporal 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.