Abstract

In this article, we discuss the regression analysis of dependent current status data under the accelerated failure time model. There exist many literatures discussing the regression analysis of current status data under different models, but few literature discussing the regression problem of dependent current status data under the AFT model. Corresponding to this, we propose a sieve maximum likelihood approach for estimation of covariate effects. In the approach, we model the correlation between the interested survival time and the observation time by the copula function. Simulation study is conducted in order to assess the finite sample behavior of the method. A real data example is provided to illustrate the application of the proposed method.

摘要

在本文中，我们讨论了加速失效时间模型下相关当前状态数据的回归分析。讨论不同模型下现状数据回归分析的文献较多，但讨论AFT模型下相关现状数据回归问题的文献较少。与此相对应，我们提出了一种用于估计协变量效应的筛最大似然方法。在该方法中，我们通过 copula 函数对感兴趣的生存时间和观察时间之间的相关性进行建模。为了评估该方法的有限样本行为，进行了模拟研究。提供了一个真实的数据示例来说明所提出方法的应用。