空间变系数Cox回归模型的贝叶斯变量选择及其在Louisiana呼吸道癌数据中的应用
Bayesian Variable Selection for Cox Regression Model with Spatially Varying Coefficients with Applications to Louisiana Respiratory Cancer Data
摘要：
Cox回归模型是生存分析中常用的模型。在公共卫生研究中，临床数据通常从不同地点的医疗服务提供者收集。协变量对特定疾病生存率的影响存在很大的地理差异。本文研究了空间变系数Cox回归模型的变量选择问题。我们提出了一个贝叶斯分层模型，它采用了马蹄事先稀疏和点质量混合之前，以确定是否回归系数是空间变化的。一个有效的两阶段计算方法用于后验推理和变量选择。它本质上是应用现有的方法来最大化部分的Cox模型的独立的网站第一，然后应用MCMC算法的变量选择的基础上的第一阶段的结果。进行了广泛的模拟研究，以检查所提出的方法的经验性能。最后，我们应用所提出的方法来分析一个真实的数据集呼吸道癌症在路易斯安那州的SEER计划。
英文摘要：
The Cox regression model is a commonly used model in survival analysis. In public health studies, clinical data are often collected from medical service providers of different locations. There are large geographical variations in the covariate effects on survival rates from particular diseases. In this paper, we focus on the variable selection issue for the Cox regression model with spatially varying coefficients. We propose a Bayesian hierarchical model which incorporates a horseshoe prior for sparsity and a point mass mixture prior to determine whether a regression coefficient is spatially varying. An efficient two-stage computational method is used for posterior inference and variable selection. It essentially applies the existing method for maximizing the partial likelihood for the Cox model by site independently first, and then applying an MCMC algorithm for variable selection based on results of the first stage. Extensive simulation studies are carried out to examine the empirical performance of the proposed method. Finally, we apply the proposed methodology to analyzing a real data set on respiratory cancer in Louisiana from the SEER program.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
