Statistics in Biopharmaceutical Research ( IF 1.8 ) Pub Date : 2020-04-17 , DOI: 10.1080/19466315.2020.1741445 Hyung Eun Lee 1 , Yang-Jin Kim 1
Abstract
In many clinical studies, the time to event of interest may involve several causes of failure. Furthermore, when the failure times are not completely observed, and instead are only known to lie somewhere between two observation times, interval censored competing risk data occur. For estimating regression coefficient with right censored competing risk data, Fine and Gray introduced the concept of censoring complete data and derived an estimating equation using an inverse probability censoring weight technique to reflect the probability being censored. As an alternative to achieve censoring complete data, Ruan and Gray considered to directly impute a potential censoring time for the subject who experienced the competing event. In this work, we extend Ruan and Gray’s approach to interval censored competing risk data by applying a multiple imputation technique. The suggested method has an advantage to be easily implemented by using several R functions developed for analyzing interval censored failure time data without competing risks. Simulation studies are conducted under diverse schemes to evaluate sizes and powers and to estimate regression coefficients. A dataset from an AIDS cohort study is analyzed as a real data example.
中文翻译:
基于非参数多重插补的区间截尾竞争风险数据分析
摘要
在许多临床研究中,感兴趣事件发生的时间可能涉及多种失败原因。此外,当故障时间没有被完全观察到,而是只知道位于两个观察时间之间的某处时,会出现间隔删失的竞争风险数据。为了用右删失的竞争风险数据估计回归系数,Fine 和 Gray 引入了删失完全数据的概念,并使用逆概率删失权重技术推导出了一个估计方程,以反映被删失的概率。作为实现删失完整数据的替代方法,Ruan 和 Gray 考虑直接为经历竞争事件的受试者估算潜在的删失时间。在这项工作中,我们通过应用多重插补技术将 Ruan 和 Gray 的方法扩展到区间删失竞争风险数据。建议的方法有一个优点,可以通过使用几个 R 函数来轻松实现,这些函数用于分析间隔删失的故障时间数据,而没有竞争风险。模拟研究在不同的方案下进行,以评估大小和功率并估计回归系数。来自 AIDS 队列研究的数据集作为真实数据示例进行分析。