Bayesian proportional hazards model for current status data with monotone splines
Bayesian proportional hazards model for current status data with monotone splines
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DOI：
10.1016/j.csda.2011.03.013
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发表时间：
2011-09-01
影响因子：
1.8
通讯作者：
Wang, Lianming
中科院分区：
文献类型：
--
作者：
Cai, Bo;Lin, Xiaoyan;Wang, Lianming
比例风险模型被广泛应用于许多领域的时间事件数据处理。然而，它的普及仅限于右删失数据，其中部分似然是可用的，部分似然方法允许人们直接估计回归系数，而无需估计基线风险函数。在本文中，我们专注于当前的状态数据，并提出了一个有效的和易于实现的比例风险模型下的贝叶斯方法。具体来说，我们用单调样条函数对基线累积风险函数进行建模，从而在保持建模灵活性的同时只需要估计有限数量的参数。提出了一种有效的吉布斯采样后验计算依赖于通过泊松潜变量的数据增强。所提出的方法进行评估和比较的约束最大似然法和其他三个现有的方法在模拟研究。子宫肌瘤的流行病学研究数据进行了分析，作为一个例子。(C)2011 Elsevier B.V.保留所有权利。
The proportional hazards model is widely used to deal with time to event data in many fields. However, its popularity is limited to right-censored data, for which the partial likelihood is available and the partial likelihood method allows one to estimate the regression coefficients directly without estimating the baseline hazard function. In this paper, we focus on current status data and propose an efficient and easy-to-implement Bayesian approach under the proportional hazards model. Specifically, we model the baseline cumulative hazard function with monotone splines leading to only a finite number of parameters to estimate while maintaining great modeling flexibility. An efficient Gibbs sampler is proposed for posterior computation relying on a data augmentation through Poisson latent variables. The proposed method is evaluated and compared to a constrained maximum likelihood method and three other existing approaches in a simulation study. Uterine fibroid data from an epidemiological study are analyzed as an illustration. (C) 2011 Elsevier B.V. All rights reserved.