In this work, we propose a new Bayesian spatial homogeneity pursuit method for survival data under the proportional hazards model to detect spatially clustered patterns in baseline hazard and regression coefficients. Specially, regression coefficients and baseline hazard are assumed to have spatial homogeneity pattern over space. To capture such homogeneity, we develop a geographically weighted Chinese restaurant process prior to simultaneously estimating coefficients and baseline hazards and their uncertainty measures. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed for our proposed methods. Performance is evaluated using simulated data, and further applied to a real data analysis of respiratory cancer in the state of Louisiana.

中文翻译：

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贝叶斯空间同质性追求生存数据与 SEER 呼吸道癌症数据的应用

在这项工作中，我们提出了一种新的贝叶斯空间同质性追踪方法，用于比例风险模型下的生存数据，以检测基线风险和回归系数中的空间聚类模式。特别是，假设回归系数和基线风险在空间上具有空间同质性模式。为了捕捉这种同质性，我们在同时估计系数和基线危害及其不确定性度量之前开发了一个地理加权的中餐馆流程。为我们提出的方法设计了一种有效的马尔可夫链蒙特卡罗（MCMC）算法。使用模拟数据评估性能，并进一步应用于路易斯安那州呼吸道癌的真实数据分析。