A quantile regression model for failure-time data with time-dependent covariates

Malka Gorfine, Yair Goldberg, Ya'acov Ritov

Research output: Contribution to journalArticlepeer-review

Abstract

Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This article provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992, Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell, N. P., Dietz, K. and Farewell, V. T. (editors), AIDS Epidemiology. Boston: Birkhaäuser, pp. 297-331.). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset.

Original languageEnglish
Pages (from-to)132-146
Number of pages15
JournalBiostatistics
Volume18
Issue number1
DOIs
StatePublished - 1 Jan 2017

Bibliographical note

Publisher Copyright:
© 2016 The Author.

Keywords

  • Instrumental variables
  • Quantile regression
  • Survival analysis
  • Time-dependent covariates

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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