Cai Zexi, Sit Tony
Department of Statistics, The Chinese University of Hong Kong, Shatin, Hong Kong.
Biometrics. 2020 Dec;76(4):1201-1215. doi: 10.1111/biom.13230. Epub 2020 Feb 28.
Quantile regression is a flexible and effective tool for modeling survival data and its relationship with important covariates, which often vary over time. Informative right censoring of data from the prevalent cohort within the population often results in length-biased observations. We propose an estimating equation-based approach to obtain consistent estimators of the regression coefficients of interest based on length-biased observations with time-dependent covariates. In addition, inspired by Zeng and Lin 2008, we also develop a more numerically stable procedure for variance estimation. Large sample properties including consistency and asymptotic normality of the proposed estimator are established. Numerical studies presented demonstrate convincing performance of the proposed estimator under various settings. The application of the proposed method is demonstrated using the Oscar dataset.
分位数回归是一种灵活且有效的工具，用于对生存数据及其与重要协变量的关系进行建模，这些协变量通常会随时间变化。对人群中现患队列的数据进行信息性右删失往往会导致长度偏倚观测值。我们提出一种基于估计方程的方法，以基于具有随时间变化协变量的长度偏倚观测值来获得感兴趣的回归系数的一致估计量。此外，受曾和林2008年研究的启发，我们还开发了一种在数值上更稳定的方差估计程序。建立了所提出估计量的包括一致性和渐近正态性在内的大样本性质。给出的数值研究证明了所提出估计量在各种设定下令人信服的性能。使用奥斯卡数据集展示了所提出方法的应用。
