Quantile regression for recurrent gap time data

Xianghua Luo, Chiung Yu Huang, Lan Wang

Research output: Contribution to journalArticle

Abstract

Evaluating covariate effects on gap times between successive recurrent events is of interest in many medical and public health studies. While most existing methods for recurrent gap time analysis focus on modeling the hazard function of gap times, a direct interpretation of the covariate effects on the gap times is not available through these methods. In this article, we consider quantile regression that can provide direct assessment of covariate effects on the quantiles of the gap time distribution. Following the spirit of the weighted risk-set method by Luo and Huang (2011, Statistics in Medicine 30, 301-311), we extend the martingale-based estimating equation method considered by Peng and Huang (2008, Journal of the American Statistical Association 103, 637-649) for univariate survival data to analyze recurrent gap time data. The proposed estimation procedure can be easily implemented in existing software for univariate censored quantile regression. Uniform consistency and weak convergence of the proposed estimators are established. Monte Carlo studies demonstrate the effectiveness of the proposed method. An application to data from the Danish Psychiatric Central Register is presented to illustrate the methods developed in this article.

Original languageEnglish (US)
Pages (from-to)375-385
Number of pages11
JournalBiometrics
Volume69
Issue number2
DOIs
StatePublished - Jun 1 2013

Keywords

  • Clustered survival data
  • Data perturbation
  • Gap times
  • Quantile regression
  • Recurrent events
  • Within-cluster resampling

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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  • Cite this

    Luo, X., Huang, C. Y., & Wang, L. (2013). Quantile regression for recurrent gap time data. Biometrics, 69(2), 375-385. https://doi.org/10.1111/biom.12010