A Bayesian MCMC approach to survival analysis with doubly-censored data.
A Bayesian MCMC approach to survival analysis with doubly-censored data.
复制标题
使用双重审查数据进行生存分析的贝叶斯 MCMC 方法。
DOI：
10.1016/j.csda.2010.02.025
复制
发表时间：
2010
影响因子：
1.8
通讯作者：
Yu,Binbing
中科院分区：
文献类型：
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作者：
Yu,Binbing
双重审查数据是指事件数据的起始时间和故障时间均被审查的时间数据。例如，在涉及艾滋病潜伏期或痴呆症发病后生存期的研究中，数据经常进行双重删失，因为起始事件的日期是间隔删失的，而失败事件的日期通常是右删失的。主要兴趣在于起始事件和故障事件之间经过时间的分布及其与暴露和风险因素的关系。估计方程方法 [Sun 等人。 （1999）。双删失失效时间数据的回归分析及其在艾滋病研究中的应用。生物识别学 55, 909–914] 及其扩展假设所有受试者的原始事件时间分布相同。本文证明了利用额外的协变量来估算原始事件时间的重要性，即，更准确地估计原始事件时间可能会减少对经过时间的参数估计的偏差。贝叶斯 MCMC 方法被证明是分析双重审查数据的合适方法，并允许丰富的生存模型。通过模拟将所提出的估计方法的性能与其他传统方法的性能进行比较。使用两个例子来说明：艾滋病队列研究和基于人群的痴呆症研究。示例代码如附录所示。
Doubly-censored data refers to time to event data for which both the originating and failure times are censored. In studies involving AIDS incubation time or survival after dementia onset, for example, data are frequently doubly-censored because the date of the originating event is interval-censored and the date of the failure event usually is right-censored. The primary interest is in the distribution of elapsed times between the originating and failure events and its relationship to exposures and risk factors. The estimating equation approach [Sun et al. (1999). Regression analysis of doubly censored failure time data with applications to AIDS studies. Biometrics 55, 909–914] and its extensions assume the same distribution of originating event times for all subjects. This paper demonstrates the importance of utilizing additional covariates to impute originating event times, i.e., more accurate estimation of originating event times may lead to less biased parameter estimates for elapsed time. The Bayesian MCMC method is shown to be a suitable approach for analyzing doubly-censored data and allows a rich class of survival models. The performance of the proposed estimation method is compared to that of other conventional methods through simulations. Two examples, an AIDS cohort study and a population-based dementia study, are used for illustration. Sample code is shown in Appendix.