Regression analysis of interval-censored failure time data with time-dependent covariates
Regression analysis of interval-censored failure time data with time-dependent covariates
复制标题
具有时间依赖性协变量的区间删失故障时间数据的回归分析
DOI：
10.1016/j.csda.2019.106848
复制
发表时间：
2020
影响因子：
1.8
通讯作者：
孙建国
中科院分区：
文献类型：
--
作者：
易凤婷;唐年胜;孙建国
区间删失故障时间数据经常出现在许多领域，其分析最近引起了广泛的关注。另一方面，大多数现有文献只能处理与时间无关的协变量。有时，人们可能会面临时间依赖性协变量，而且协变量也可能遭受测量误差。针对这种情况，一种方法是进行联合分析，文献中已经在不同框架下开发了许多方法。这些方法的一个缺点是，它们通常假设在故障时间之后不再对协变量进行测量，但显然这可能不是真的。本文提出了一种新的联合分析方法，可以考虑额外的观察结果。特别是，对于估计，我们开发了一种 MCEM 算法，它比现有算法更稳定且收敛速度更快。为了评估所提出方法的有限样本性能，进行了广泛的模拟研究，并表明该方法在实际情况下效果很好。该方法还应用于引发本次调查的艾滋病研究。
Interval-censored failure time data often occur in many areas and their analysis has recently attracted a great deal of attention. On the other hand, most of the existing literature for them can only deal with time-independent covariates. Sometimes one may face time dependent covariates and furthermore the covariates could also suffer measurement errors. For the situation, one approach is to conduct a joint analysis for which many methods have been developed in the literature under various framework. One drawback of these methods is that they usually assume that there are no more measurements on the covariates after the failure time and it is apparent that this may not be true. In this paper, a new joint analysis approach is proposed that can take into account the extra observations. In particular, for estimation, a MCEM algorithm is developed that is much more stable and converges much faster than the existing algorithms. To assess the finite sample performance of the proposed method, an extensive simulation study is conducted and suggests that it works well for practical situations. Also the method is applied to an AIDS study that motivated this investigation.