Research Center of Applied Statistics and Big Data, Huaqiao University, Xiamen, China.
School of Statistics, Huaqiao University, Xiamen, China.
Stat Methods Med Res. 2021 May;30(5):1320-1331. doi: 10.1177/0962280221995986. Epub 2021 Apr 7.
The quantile regression model has increasingly become a useful approach for analyzing survival data due to its easy interpretation and flexibility in exploring the dynamic relationship between a time-to-event outcome and the covariates. In this paper, we consider the quantile regression model for survival data with missing censoring indicators. Based on the augmented inverse probability weighting technique, two weighted estimating equations are developed and corresponding easily implemented algorithms are suggested to solve the estimating equations. Asymptotic properties of the resultant estimators and the resampling-based inference procedures are established. Finally, the finite sample performances of the proposed approaches are investigated in simulation studies and a real data application.
由于其易于解释和灵活探索事件时间结果与协变量之间动态关系的特点，分位数回归模型在分析生存数据方面越来越成为一种有用的方法。本文考虑了带有缺失删失指示符的生存数据的分位数回归模型。基于增广逆概率加权技术，提出了两个加权估计方程，并建议了相应的易于实现的算法来求解估计方程。建立了所得估计量的渐近性质和基于重抽样的推断程序。最后，在模拟研究和实际数据应用中研究了所提出方法的有限样本性能。
