Peng Limin
Department of Biostatistics and Bioinformatics, Emory University, Atlanta, USA, 30322.
Annu Rev Stat Appl. 2021 Mar;8(1):413-437. doi: 10.1146/annurev-statistics-042720-020233.
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.
分位数回归为分析生存数据提供了一种有用的替代策略。与传统的生存分析方法相比，分位数回归能够对协变量对感兴趣的生存结果的影响进行全面且灵活的评估，同时在时间尺度上给出简单直观的解释。此外，许多分位数回归方法具有易于计算且稳定的特点。这些吸引人的特性使分位数回归成为对生存数据进行深入分析的有价值的实用工具。在本文中，我回顾了一套全面的统计方法，用于对不同类型的生存数据进行分位数回归。本综述涵盖了各种生存场景，包括随机删失数据、左截断或删失数据、竞争风险和半竞争风险数据以及复发事件数据。给出了两个实际例子来说明分位数回归在实际生存数据分析中的效用。
