Lifetime Data Analysis

, Volume 18, Issue 2, pp 177–194

Censored quantile regression for residual lifetimes



We propose a regression method that studies covariate effects on the conditional quantiles of residual lifetimes at a certain followup time point. This can be particularly useful in cancer studies, where more patients survive cancers initially and a patient’s residual life expectancy is used to compare the efficacy of secondary or adjuvant therapies. The new method provides a consistent estimator that often exhibits smaller standard error in real and simulated examples, compared to the existing method of Jung et al. (2009). It also provides a simple empirical likelihood inference method that does not require estimating the covariance matrix of the estimator or resampling. We apply the new method to a breast cancer study (NSABP Protocol B-04, Fisher et al. (2002)) and estimate median residual lifetimes at various followup time points, adjusting for important prognostic factors.


Cancer Empirical likelihood Quantile regression Residual lifetime regression Survival analysis Wilks’ theorem 


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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Cincinnati Children’s Medical CenterCincinnatiUSA
  2. 2.University of KentuckyLexingtonUSA
  3. 3.University of PittsburghPittsburghUSA

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