Quantile Regression for Survival Analysis

Authored by: Limin Peng

Handbook of Quantile Regression

Print publication date:  October  2017
Online publication date:  October  2017

Print ISBN: 9781498725286
eBook ISBN: 9781315120256
Adobe ISBN:

10.1201/9781315120256-7

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Abstract

Quantile regression has natural appeals in model flexibility and interpretability. It has received increased attention in survival analysis because event times themselves are often of scientific interest, and quantiles are more flexible and robust quantitative tools for characterizing event times than mean-based devices. In addition, quantile regression permits investigating particular local features of the conditional distribution of an event time of interest. The usefulness of quantile regression for survival analysis has been demonstrated by many applications reported in literature. For example, Koenker and Geling (2001) provided a detailed quantile regression analysis of a medfly longevity data, which illustrates how to use quantile regression to assess and interpret covariate effects on different segments of the event time distribution. Such a capacity constitutes another major advantage of quantile regression over conventional survival models, such as the proportional hazards model and the accelerated failure time model, which implicitly exert pure location shift effects on survival times or monotone transformations thereof.

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