Quantile Regression for Doubly Censored Data
Version of Record online: 27 SEP 2011
© 2011, The International Biometric Society
Volume 68, Issue 1, pages 101–112, March 2012
How to Cite
Ji, S., Peng, L., Cheng, Y. and Lai, H. (2012), Quantile Regression for Doubly Censored Data. Biometrics, 68: 101–112. doi: 10.1111/j.1541-0420.2011.01667.x
- Issue online: 23 MAR 2012
- Version of Record online: 27 SEP 2011
- Received November 2010. Revised June 2011., Accepted June 2011.
- Conditional inference;
- Double censoring;
- Empirical process;
- Regression quantile;
Summary Double censoring often occurs in registry studies when left censoring is present in addition to right censoring. In this work, we propose a new analysis strategy for such doubly censored data by adopting a quantile regression model. We develop computationally simple estimation and inference procedures by appropriately using the embedded martingale structure. Asymptotic properties, including the uniform consistency and weak convergence, are established for the resulting estimators. Moreover, we propose conditional inference to address the special identifiability issues attached to the double censoring setting. We further show that the proposed method can be readily adapted to handle left truncation. Simulation studies demonstrate good finite-sample performance of the new inferential procedures. The practical utility of our method is illustrated by an analysis of the onset of the most commonly investigated respiratory infection, Pseudomonas aeruginosa, in children with cystic fibrosis through the use of the U.S. Cystic Fibrosis Registry.