Quantile Regression for Right-.Censored and Length-Biased Data

• Published in: Acta Mathematicae Applicatae Sinica 2012-07, Vol.28 (3), p.443-462
• Main Authors: Chen, Xue-rong, Zhou, Yong
• Format: Article
• Language: English
• Subjects: Applications of Mathematics; Math Applications in Computer Science; Mathematical and Computational Physics; Mathematics; Mathematics and Statistics; Theoretical; 偏置; 分位数; 加权估计; 回归方法; 采样方法; 长度偏差; 队列研究; 鼠标右键
• ISSN: 0168-9673; 1618-3932
• DOI: 10.1007/s10255-012-0157-3

Length-biased data arise in many important fields, including epidemiological cohort studies, cancer screening trials and labor economics. Analysis of such data has attracted much attention in the literature. In this paper we propose a quantile regression approach for analyzing right-censored and length-biased data. We derive an inverse probability weighted estimating equation corresponding to the quantile regression to correct the bias due to length-bias sampling and informative censoring. This method can easily handle informative censoring induced by length-biased sampling. This is an appealing feature of our proposed method since it is generally difficult to obtain unbiased estimates of risk factors in the presence of length-bias and informative censoring. We establish the consistency and asymptotic distribution of the proposed estimator using empirical process techniques. A resampling method is adopted to estimate the variance of the estimator. We conduct simulation studies to evaluate its finite sample performance and use a real data set to illustrate the application of the proposed method.