Quantile Regression for Survival Data

Quantile regression offers a useful alternative strategy for analyzing survival data. Compared with 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. This article provides a review of a comprehensive set of statistical methods for performing quantile regression with different types of survival data. The review covers various survival scenarios, including randomly censored data, data subject to left truncation or censoring, competing risks and semicompeting risks data, and recurrent events data. Two real-world examples are presented to illustrate the utility of quantile regression for practical survival data analyses.

Automated summary

Quantile regression provides a useful alternative strategy for analyzing survival data, allowing for comprehensive evaluations of covariate effects with simple physical interpretations and easy computation. This article reviews a comprehensive set of statistical methods for performing quantile regression with different types of survival data, demonstrating the practical utility of this approach through two real-world examples.