Spatially explicit survival modeling for small area cancer data.
Spatially explicit survival modeling for small area cancer data.
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小区域癌症数据的空间明确生存模型。
DOI：
10.1080/02664763.2017.1288200
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发表时间：
2018
影响因子：
1.5
通讯作者：
Eberth,JM
中科院分区：
文献类型：
--
作者：
Onicescu,G;Lawson,A;Zhang,J;Gebregziabher,Mulugeta;Wallace,Kristin;Eberth,JM
在本文中，我们提出了一种新的贝叶斯统计方法的空间生存数据。我们的方法拓宽了生存，密度和危险函数的定义，明确建模的空间依赖性，使用这些功能及其边缘和条件的直接推导。我们还推导出空间相关的似然函数。最后，我们研究了这些推导的应用与地理增强生存分布的路易斯安那州的监测，流行病学和最终结果登记前列腺癌的数据。
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 registry prostate cancer data.