论文标题
使用具有自适应时间分区的伽马过程的贝叶斯生存分析
Bayesian Survival Analysis Using Gamma Processes with Adaptive Time Partition
论文作者
论文摘要
在贝叶斯半参数分析的事件数据数据中，用于基线危害函数或累积基线危害函数的非参数过程先验，用于时间轴的给定有限分区。但是，提出一个构建最佳时间分区的一般指南将是有争议的。虽然已经进行了大量研究来放松其他非参数过程的固定拆分时间的假设，但据我们所知，尚未开发用于伽马过程的方法，这是贝叶斯生存分析中最广泛使用的方法之一。在本文中，我们提出了一个新的贝叶斯框架，用于比例危害模型，其中累积基线危害函数是通过伽马过程对先验进行建模的。提出的框架的一个关键特征是，间隔切割点的数量和位置根据其后验分布视为随机和估计。
In Bayesian semi-parametric analyses of time-to-event data, non-parametric process priors are adopted for the baseline hazard function or the cumulative baseline hazard function for a given finite partition of the time axis. However, it would be controversial to suggest a general guideline to construct an optimal time partition. While a great deal of research has been done to relax the assumption of the fixed split times for other non-parametric processes, to our knowledge, no methods have been developed for a gamma process prior, which is one of the most widely used in Bayesian survival analysis. In this paper, we propose a new Bayesian framework for proportional hazards models where the cumulative baseline hazard function is modeled a priori by a gamma process. A key feature of the proposed framework is that the number and position of interval cutpoints are treated as random and estimated based on their posterior distributions.
