Wang Peijie, Zhao Hui, Sun Jianguo
School of Mathematics, Jilin University, Changchun 130012, China.
School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan 430079, China.
Biometrics. 2016 Dec;72(4):1103-1112. doi: 10.1111/biom.12527. Epub 2016 Apr 28.
Interval-censored failure time data occur in many fields such as demography, economics, medical research, and reliability and many inference procedures on them have been developed (Sun, 2006; Chen, Sun, and Peace, 2012). However, most of the existing approaches assume that the mechanism that yields interval censoring is independent of the failure time of interest and it is clear that this may not be true in practice (Zhang et al., 2007; Ma, Hu, and Sun, 2015). In this article, we consider regression analysis of case K interval-censored failure time data when the censoring mechanism may be related to the failure time of interest. For the problem, an estimated sieve maximum-likelihood approach is proposed for the data arising from the proportional hazards frailty model and for estimation, a two-step procedure is presented. In the addition, the asymptotic properties of the proposed estimators of regression parameters are established and an extensive simulation study suggests that the method works well. Finally, we apply the method to a set of real interval-censored data that motivated this study.
区间删失失效时间数据出现在许多领域,如人口统计学、经济学、医学研究和可靠性研究等,并且针对这些数据已经开发了许多推断程序(Sun,2006;Chen,Sun和Peace,2012)。然而,现有的大多数方法都假定产生区间删失的机制与感兴趣的失效时间无关,而在实际中这显然可能不成立(Zhang等,2007;Ma,Hu和Sun,2015)。在本文中,当删失机制可能与感兴趣的失效时间相关时,我们考虑对K个案例的区间删失失效时间数据进行回归分析。针对该问题,我们针对来自比例风险脆弱模型的数据提出了一种估计的筛最大似然方法,并给出了一个两步估计程序。此外,我们建立了所提出的回归参数估计量的渐近性质,并且广泛的模拟研究表明该方法效果良好。最后,我们将该方法应用于一组激发本研究的实际区间删失数据。