论文标题
贝叶斯空间同质性追求生存数据，并应用于SEER呼吸道癌数据
Bayesian Spatial Homogeneity Pursuit for Survival Data with an Application to the SEER Respiratory Cancer Data
论文作者
论文摘要
在这项工作中，我们提出了一种新的贝叶斯空间均匀性追求方法，用于根据比例危害模型的生存数据，以检测基线危险和回归系数中的空间簇模式。特别是，假定回归系数和基线危险在空间上具有空间同质性模式。为了捕获这种同质性，我们在同时估计系数和基线危害及其不确定性措施之前，开发了地理位置加权的中国餐厅过程。有效的马尔可夫链蒙特卡洛（MCMC）算法是为我们提出的方法设计的。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癌的真实数据分析。
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.
