Carroll Rachel, Lawson Andrew B, Kirby Russell S, Faes Christel, Aregay Mehreteab, Watjou Kevin
Department of Public Health, Medical University of South Carolina, Charleston.
Department of Public Health, Medical University of South Carolina, Charleston.
Ann Epidemiol. 2017 Jan;27(1):42-51. doi: 10.1016/j.annepidem.2016.08.014. Epub 2016 Aug 31.
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.
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.
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.
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.
许多类型的癌症都有潜在的时空分布。时空混合建模可以通过纳入潜在变量提供一种灵活的风险估计方法。
在本文中，我们研究了四种不同的时空混合建模方法在南卡罗来纳州肺癌和支气管癌以及“其他”呼吸道癌症发病率建模中的应用和益处。
在所测试的方法中，没有一种方法优于其他方法；哪种方法最佳取决于所考虑的癌症类型。单变量建模公式最能描述肺癌和支气管癌的发病率结果，而多变量建模公式最能描述“其他”呼吸道癌症的发病率结果。
当纳入来自相关的、更常见癌症类型的信息时，时空多变量混合方法有助于对发病率低且数据稀疏的癌症进行建模。
