Analysis of interval censored competing risk data via nonparametric multiple imputation

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 (1999) introduced the concept of censoring complete data and derived an estimating equation using an IPCW (Inverse probability censoring weight) technique to reflect the probability being censored. As an alternative to achieve censoring complete data, Ruan and Gray (2008) 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 data set from an AIDS cohort study is analyzed as a real data example.

在诸多临床研究中，目标事件的发生时间往往涉及多种失效原因。此外，当失效时间未被完全观测，仅可知其介于两次观测时间之间时，便会出现区间截尾竞争风险数据（interval censored competing risk data）。针对右截尾竞争风险数据的回归系数估计问题，Fine与Gray（1999）提出了删失完备数据的概念，并采用IPCW（Inverse probability censoring weight，逆概率删失加权）技术推导得到估计方程，以反映个体被删失的概率。作为实现删失完备数据的另一途径，Ruan与Gray（2008）提出直接为经历竞争事件的受试者估算潜在删失时间。本研究将Ruan与Gray的方法拓展至区间截尾竞争风险数据场景，采用多重插补技术完成这一拓展。所提方法具备易实现性优势，可借助多款专为无竞争风险的区间截尾失效时间数据分析开发的R函数完成运算。本研究通过多种仿真方案开展仿真实验，以评估检验水准与检验效能，并估算回归系数。最后，本研究以一项艾滋病队列研究的数据集作为真实数据示例展开分析。