Zhao Lili, Feng Dai, Bellile Emily L, Taylor Jeremy M G
Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A.
Stat Med. 2014 Feb 20;33(4):650-61. doi: 10.1002/sim.5964. Epub 2013 Sep 6.
In this paper, we develop a Bayesian approach to estimate a Cox proportional hazards model that allows a threshold in the regression coefficient, when some fraction of subjects are not susceptible to the event of interest. A data augmentation scheme with latent binary cure indicators is adopted to simplify the Markov chain Monte Carlo implementation. Given the binary cure indicators, the Cox cure model reduces to a standard Cox model and a logistic regression model. Furthermore, the threshold detection problem reverts to a threshold problem in a regular Cox model. The baseline cumulative hazard for the Cox model is formulated non-parametrically using counting processes with a gamma process prior. Simulation studies demonstrate that the method provides accurate point and interval estimates. Application to a data set of oropharynx cancer patients suggests a significant threshold in age at diagnosis such that the effect of gender on disease-specific survival changes after the threshold.
在本文中，我们开发了一种贝叶斯方法来估计Cox比例风险模型，当一部分受试者对感兴趣的事件不敏感时，该模型允许回归系数存在一个阈值。采用带有潜在二元治愈指标的数据增强方案来简化马尔可夫链蒙特卡罗实现。给定二元治愈指标，Cox治愈模型简化为标准Cox模型和逻辑回归模型。此外，阈值检测问题转化为常规Cox模型中的阈值问题。Cox模型的基线累积风险使用具有伽马过程先验的计数过程进行非参数化表述。模拟研究表明该方法提供了准确的点估计和区间估计。应用于一组口咽癌患者数据集表明，诊断年龄存在显著阈值，使得阈值之后性别对疾病特异性生存的影响发生变化。
