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April 2009 Inference for censored quantile regression models in longitudinal studies
Huixia Judy Wang, Mendel Fygenson
Ann. Statist. 37(2): 756-781 (April 2009). DOI: 10.1214/07-AOS564


We develop inference procedures for longitudinal data where some of the measurements are censored by fixed constants. We consider a semi-parametric quantile regression model that makes no distributional assumptions. Our research is motivated by the lack of proper inference procedures for data from biomedical studies where measurements are censored due to a fixed quantification limit. In such studies the focus is often on testing hypotheses about treatment equality. To this end, we propose a rank score test for large sample inference on a subset of the covariates. We demonstrate the importance of accounting for both censoring and intra-subject dependency and evaluate the performance of our proposed methodology in a simulation study. We then apply the proposed inference procedures to data from an AIDS-related clinical trial. We conclude that our framework and proposed methodology is very valuable for differentiating the influences of predictors at different locations in the conditional distribution of a response variable.


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Huixia Judy Wang. Mendel Fygenson. "Inference for censored quantile regression models in longitudinal studies." Ann. Statist. 37 (2) 756 - 781, April 2009.


Published: April 2009
First available in Project Euclid: 10 March 2009

zbMATH: 1162.62035
MathSciNet: MR2502650
Digital Object Identifier: 10.1214/07-AOS564

Primary: 62G99
Secondary: 62N01 , 62P10

Keywords: Fixed censoring , logitudinal data , Quantile regression , rank score test , Tobit model , viral load

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.37 • No. 2 • April 2009
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