Diva Ulysses, Banerjee Sudipto, Dey Dipak K
Global Biometric Sciences, Bristol-Myers Squibb Company, US.
Stat Modelling. 2007 Jul 1;7(2):191-213. doi: 10.1177/1471082X0700700205.
Epidemiologists and biostatisticians investigating spatial variation in diseases are often interested in estimating spatial effects in survival data, where patients are monitored until their time to failure (for example, death, relapse). Spatial variation in survival patterns often reveals underlying lurking factors, which, in turn, assist public health professionals in their decision-making process to identify regions requiring attention. The Surveillance Epidemiology and End Results (SEER) database of the National Cancer Institute provides a fairly sophisticated platform for exploring novel approaches in modelling cancer survival, particularly with models accounting for spatial clustering and variation. Modelling survival data for patients with multiple cancers poses unique challenges in itself and in capturing the spatial associations of the different cancers. This paper develops the Bayesian hierarchical survival models for capturing spatial patterns within the framework of proportional hazard. Spatial variation is introduced in the form of county-cancer level frailties. The baseline hazard function is modelled semiparametrically using mixtures of beta distributions. We illustrate with data from the SEER database, perform model checking and comparison among competing models, and discuss implementation issues.
研究疾病空间变异的流行病学家和生物统计学家通常对估计生存数据中的空间效应感兴趣，在这类数据中，患者会被监测直至出现失败时间（例如死亡、复发）。生存模式的空间变异往往揭示潜在的隐藏因素，进而有助于公共卫生专业人员在决策过程中识别需要关注的区域。美国国家癌症研究所的监测、流行病学和最终结果（SEER）数据库为探索癌症生存建模的新方法提供了一个相当完善的平台，特别是对于考虑空间聚类和变异的模型。对患有多种癌症的患者的生存数据进行建模本身就带来了独特的挑战，并且在捕捉不同癌症的空间关联方面也存在挑战。本文在比例风险框架内开发了用于捕捉空间模式的贝叶斯分层生存模型。空间变异以县 - 癌症水平的脆弱性形式引入。基线风险函数使用贝塔分布的混合进行半参数建模。我们用SEER数据库的数据进行说明，对竞争模型进行模型检验和比较，并讨论实施问题。
