ABSTRACT

In this study, we examined the problem of constructing a model for time-to-event data considering dependent censoring. Our goal was to construct a set of subgroups of covariate space, wherein each element had the same failure model considering the dependency of failure and censoring times. As such, a model was constructed based on the parametric form from the identifiability problem of censoring. We used the copula to represent the dependency between failure and censoring times. Under the assumption of parametric models for failure and censoring times and a copula function, which have unknown parameters, we proposed a method for constructing the tree-structured model through the test statistics. We subsequently evaluated the performance of the splitting rule and tree obtained using the proposed method and compared it with the general method that assumes independent censoring through simulation studies. We also present the analysis results for AIDS clinical trial research to show the utility of the method.

摘要

在本研究中，我们研究了考虑相关删失的事件时间数据模型的构建问题。我们的目标是构建一组协变量空间的子组，其中考虑到故障和审查时间的依赖性，每个元素都具有相同的故障模型。因此，基于审查的可识别性问题的参数形式构建模型。我们使用 copula 来表示失败和审查时间之间的依赖关系。在故障和审查次数的参数模型和具有未知参数的copula函数的假设下，我们提出了一种通过检验统计量构建树结构模型的方法。我们随后评估了使用所提出的方法获得的分裂规则和树的性能，并将其与通过模拟研究假设独立审查的一般方法进行了比较。我们还展示了艾滋病临床试验研究的分析结果，以展示该方法的实用性。