论文标题
与空间变化系数的贝叶斯加速故障时间模型的比较
A comparison of Bayesian accelerated failure time models with spatially varying coefficients
论文作者
论文摘要
加速故障时间（AFT）模型是分析生存数据的常用工具。在公共卫生研究中，通常从不同地点的医疗服务提供商那里收集数据。来自不同位置的存活率通常呈现在地理上不同的模式。在本文中，我们关注具有空间变化系数的加速故障时间模型。我们比较了三种类型的先验，用于空间变化的系数。开发了一个模型选择标准，即伪划分可能性（LPML）的对数，以评估具有不同先验的AFT模型的拟合度。进行了广泛的仿真研究以检查所提出方法的经验性能。最后，我们将模型应用于路易斯安那州前列腺癌的SEER数据，并证明了前列腺癌数据对生存率的空间变化影响。
The accelerated failure time (AFT) model is a commonly used tool in analyzing survival data. In public health studies, data is often collected from medical service providers in different locations. Survival rates from different locations often present geographically varying patterns. In this paper, we focus on the accelerated failure time model with spatially varying coefficients. We compare three types of the priors for spatially varying coefficients. A model selection criterion, logarithm of the pseudo-marginal likelihood (LPML), is developed to assess the fit of AFT model with different priors. Extensive simulation studies are carried out to examine the empirical performance of the proposed methods. Finally, we apply our model to SEER data on prostate cancer in Louisiana and demonstrate the existence of spatially varying effects on survival rates from prostate cancer data.
