Banerjee Sudipto, Dey Dipak K
Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
Lifetime Data Anal. 2005 Jun;11(2):175-91. doi: 10.1007/s10985-004-0382-z.
The last decade has witnessed major developments in Geographical Information Systems (GIS) technology resulting in the need for statisticians to develop models that account for spatial clustering and variation. In public health settings, epidemiologists and health-care professionals are interested in discerning spatial patterns in survival data that might exist among the counties. This paper develops a Bayesian hierarchical model for capturing spatial heterogeneity within the framework of proportional odds. This is deemed more appropriate when a substantial percentage of subjects enjoy prolonged survival. We discuss the implementation issues of our models, perform comparisons among competing models and illustrate with data from the SEER (Surveillance Epidemiology and End Results) database of the National Cancer Institute, paying particular attention to the underlying spatial story.
过去十年见证了地理信息系统（GIS）技术的重大发展，这使得统计学家需要开发能够考虑空间聚类和变异的模型。在公共卫生领域，流行病学家和医疗保健专业人员有兴趣识别各县之间可能存在的生存数据中的空间模式。本文开发了一种贝叶斯分层模型，用于在比例优势框架内捕捉空间异质性。当相当大比例的受试者享有延长生存期时，这被认为更合适。我们讨论了模型的实施问题，在竞争模型之间进行了比较，并以美国国家癌症研究所的监测、流行病学和最终结果（SEER）数据库中的数据为例进行说明，特别关注潜在的空间情况。
