Onicescu G, Lawson A, Zhang J, Gebregziabher Mulugeta, Wallace Kristin, Eberth J M
Department of Statistics, Western Michigan University, Kalamazoo, MI.
Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC.
J Appl Stat. 2018;45(3):568-585. doi: 10.1080/02664763.2017.1288200. Epub 2017 Feb 11.
In this paper we propose a novel Bayesian statistical methodology for spatial survival data. Our methodology broadens the definition of the survival, density and hazard functions by explicitly modeling the spatial dependency using direct derivations of these functions and their marginals and conditionals. We also derive spatially dependent likelihood functions. Finally we examine the applications of these derivations with geographically augmented survival distributions in the context of the Louisiana Surveillance, Epidemiology, and End Results (SEER) registry prostate cancer data.
在本文中，我们提出了一种用于空间生存数据的新颖贝叶斯统计方法。我们的方法通过使用这些函数及其边缘和条件的直接推导来明确建模空间依赖性，从而拓宽了生存、密度和风险函数的定义。我们还推导了空间依赖的似然函数。最后，我们在路易斯安那州监测、流行病学和最终结果（SEER）登记处前列腺癌数据的背景下，研究了这些推导在地理增强生存分布中的应用。
