Quantile regression and empirical likelihood for the analysis of longitudinal data with monotone missing responses due to dropout, with applications to quality of life measurements from clinical trials. (17th April 2019)

• Record Type: Journal Article
• Authors: Lv, Yang; Qin, Guoyou; Zhu, Zhongyi; Tu, Dongsheng
• Is Part Of: Statistics in medicine. Volume 38:Number 16(2019)
• Page Start: 2972
• Page End: 2991
• Publication Date: 2019-04-17
• Subjects: auxiliary information -- empirical likelihood -- inverse probability weight -- longitudinal data -- missing at random -- quantile regression; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8152
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : The analysis of quality of life (QoL) data can be challenging due to the skewness of responses and the presence of missing data. In this paper, we propose a new weighted quantile regression method for estimating the conditional quantiles of QoL data with responses missing at random. The proposed method makes use of the correlation information within the same subject from an auxiliary mean regression model to enhance the estimation efficiency and takes into account of missing data mechanism. The asymptotic properties of the proposed estimator have been studied and simulations are also conducted to evaluate the performance of the proposed estimator. The proposed method has also been applied to the analysis of the QoL data from a clinical trial on early breast cancer, which motivated this study.