Huang Yijian, Peng Limin
Department of Biostatistics and Bioinformatics, Emory University.
Scand Stat Theory Appl. 2009 Dec 1;36(4):636. doi: 10.1111/j.1467-9469.2009.00645.x.
For the analysis with recurrent events, we propose a generalization of the accelerated failure time model to allow for evolving covariate effects. These so-called accelerated recurrence time models postulate that time to expected recurrence frequency, upon transformation, is a linear function of covariates with frequency-dependent coefficients. This modeling strategy shares the same spirit as quantile regression. An estimation and inference procedure is developed by generalizing the celebrated Powell's (1984, 1986) estimator for censored quantile regression. Consistency and asymptotic normality of the proposed estimator are established. An algorithm is devised to attain good computational efficiency. Simulations demonstrate that this proposal performs well under practical settings. This methodology is illustrated in an application to the well-known bladder cancer study.
对于复发事件的分析，我们提出了加速失效时间模型的一种推广形式，以考虑随时间变化的协变量效应。这些所谓的加速复发时间模型假定，经变换后，预期复发频率的时间是具有频率依赖系数的协变量的线性函数。这种建模策略与分位数回归具有相同的理念。通过推广著名的鲍威尔（1984年、1986年）删失分位数回归估计量，开发了一种估计和推断程序。建立了所提估计量的一致性和渐近正态性。设计了一种算法以实现良好的计算效率。模拟表明，该方法在实际情况下表现良好。在对著名的膀胱癌研究的应用中展示了这种方法。
