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

• Published in: Statistics in medicine Vol. 38; no. 16; pp. 2972 - 2991
• Main Authors: Lv, Yang, Qin, Guoyou, Zhu, Zhongyi, Tu, Dongsheng
• Format: Journal Article
• Language: English
• Published: England Wiley Subscription Services, Inc 20.07.2019
• Subjects: auxiliary information; Breast cancer; Clinical trials; empirical likelihood; inverse probability weight; longitudinal data; Longitudinal studies; missing at random; Quality of life; quantile regression; Regression analysis; Statistical methods; quantile regression; missing at random; empirical likelihood; inverse probability weight; auxiliary information; longitudinal data
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.8152

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.