A censored quantile regression approach for the analysis of time to event data. (March 2018)

• Record Type: Journal Article
• Authors: Xue, Xiaonan; Xie, Xianhong; Strickler, Howard D
• Other Names: Nakas Christos T guest-editor.; Reiser Benjamin guest-editor.
• Is Part Of: Statistical methods in medical research. Volume 27:Number 3(2018)
• Page Start: 955
• Page End: 965
• Publication Date: 2018-03
• Subjects: Proportionality -- prediction -- validation -- cross-validation -- quantile -- hazards ratio -- accelerated failure time model; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216648724
• Languages: English
• ISSNs: 0962-2802

Abstract

The commonly used statistical model for studying time to event data, the Cox proportional hazards model, is limited by the assumption of a constant hazard ratio over time (i.e., proportionality), and the fact that it models the hazard rate rather than the survival time directly. The censored quantile regression model, defined on the quantiles of time to event, provides an alternative that is more flexible and interpretable. However, the censored quantile regression model has not been widely adopted in clinical research, due to the complexity involved in interpreting its results properly and consequently the difficulty to appreciate its advantages over the Cox proportional hazards model, as well as the absence of adequate validation procedure. In this paper, we addressed these limitations by (1) using both simulated examples and data from National Wilms' Tumor clinical trials to illustrate proper interpretation of the censored quantile regression model and the differences and the advantages of the model compared to the Cox proportional hazards model; and (2) developing a validation procedure for the predictive censored quantile regression model. The performance of this procedure was examined using simulation studies. Overall, we recommend the use of censored quantile regression model, which permits a more sensitive analysis of time to event data together with the Cox proportional hazards model.