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 estimate 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.

翻译：在这项工作中,我们提议在比例危害模型下对生存数据采用新的巴耶斯空间同质搜索方法,以探测基线危险和回归系数中的空间集群模式。特别是,假设回归系数和基线危险具有空间同质模式;为了捕捉这种同质性,我们在同时估计系数和基线危险及其不确定性措施之前,先开发一个地理加权的中国餐馆流程。为我们拟议的方法设计了一个高效的Markov连锁Monte Carlo(MCMC)算法。利用模拟数据对绩效进行评估,并进一步应用于路易斯安那州呼吸道癌的实际数据分析。