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 the time to expected recurrence frequency, upon transformation, is a linear function of covariates with frequency-dependent coefficients. This modelling strategy shares the same spirit as quantile regression. An estimation and inference procedure is developed by generalizing the celebrated Powell's ("J. Econometrics" 25, 1984, 303; "J. Econometrics" 32, 1986, 143) 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. Copyright (c) 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
展开▼
机译：对于周期性事件的分析，我们提出了加速失效时间模型的一般化，以允许演化的协变量效应。这些所谓的加速递归时间模型假定，预期的递归频率的时间在变换后是与频率相关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精神。通过推广著名的鲍威尔（“ J. Econometrics” 25，1984，303;“ J. Econometrics” 32，1986，143）估计器来发展被删减分位数回归的估计和推理程序。建立了所提出估计量的一致性和渐近正态性。设计了一种算法来获得良好的计算效率。仿真表明，该建议在实际设置下效果良好。该方法在著名膀胱癌研究中的应用中得到了说明。斯堪的纳维亚统计杂志基金会（c）2009理事会。
展开▼
