Quantile regression offers a useful alternative strategy for analyzing survival data. Compared to traditional survival analysis methods, quantile regression allows for comprehensive and flexible evaluations of covariate effects on a survival outcome of interest, while providing simple physical interpretations on the time scale. Moreover, many quantile regression methods enjoy easy and stable computation. These appealing features make quantile regression a valuable practical tool for delivering in-depth analyses of survival data. In this paper, I review a comprehensive set of statistical methods for performing quantile regression with different types of survival data. This review covers various survival scenarios, including randomly censored data, data subject to left truncation or censoring, competing risks and semi-competing risks data, and recurrent events data. Two real examples are presented to illustrate the utility of quantile regression for practical survival data analyses.
Keywords: Quantile regression; competing risks data; estimating equation; randomly censored data; recurrent events data; semi-competing risks data.