Luo Xianghua, Huang Chiung-Yu, Wang Lan
Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
Biometrics. 2013 Jun;69(2):375-85. doi: 10.1111/biom.12010. Epub 2013 Mar 11.
Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301-311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637-649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article.
在许多医学和公共卫生研究中，评估协变量对连续复发事件之间间隔时间的影响很有意义。虽然大多数现有的复发间隔时间分析方法侧重于对间隔时间的风险函数进行建模，但通过这些方法无法直接解释协变量对间隔时间的影响。在本文中，我们考虑分位数回归，它可以直接评估协变量对间隔时间分布分位数的影响。遵循Luo和Huang（2011年，《医学统计学》30卷，301 - 311页）加权风险集方法的精神，我们将Peng和Huang（2008年，《美国统计协会杂志》103卷，637 - 649页）针对单变量生存数据考虑的基于鞅的估计方程方法扩展到分析复发间隔时间数据。所提出的估计程序可以在现有的单变量删失分位数回归软件中轻松实现。建立了所提出估计量的一致收敛性和弱收敛性。蒙特卡罗研究证明了所提出方法的有效性。给出了对丹麦精神病学中央登记处数据的应用，以说明本文中开发的方法。
