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 2013
Externally publishedYes

Fingerprint

Quantile Regression
Public health
Medicine
Hazards
Statistics
Covariates
Univariate
methodology
Censored Regression
Uniform Consistency
Recurrent Events
Monte Carlo method
Hazard Function
Estimating Equation
Survival Data
Public Health
Monte Carlo Study
Quantile
Weak Convergence
Martingale

Keywords

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

ASJC Scopus subject areas

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

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

Quantile regression for recurrent gap time data. / Luo, Xianghua; Huang, Chiung Yu; Wang, Lan.

In: Biometrics, Vol. 69, No. 2, 06.2013, p. 375-385.

Research output: Contribution to journalArticle

Luo, X, Huang, CY & Wang, L 2013, 'Quantile regression for recurrent gap time data', Biometrics, vol. 69, no. 2, pp. 375-385. https://doi.org/10.1111/biom.12010
Luo, Xianghua ; Huang, Chiung Yu ; Wang, Lan. / Quantile regression for recurrent gap time data. In: Biometrics. 2013 ; Vol. 69, No. 2. pp. 375-385.
@article{d70fa3f7d28f43fc80adf0fd329a91bd,
title = "Quantile regression for recurrent gap time data",
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.",
keywords = "Clustered survival data, Data perturbation, Gap times, Quantile regression, Recurrent events, Within-cluster resampling",
author = "Xianghua Luo and Huang, {Chiung Yu} and Lan Wang",
year = "2013",
month = "6",
doi = "10.1111/biom.12010",
language = "English (US)",
volume = "69",
pages = "375--385",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "2",

}

TY - JOUR

T1 - Quantile regression for recurrent gap time data

AU - Luo, Xianghua

AU - Huang, Chiung Yu

AU - Wang, Lan

PY - 2013/6

Y1 - 2013/6

N2 - 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.

AB - 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.

KW - Clustered survival data

KW - Data perturbation

KW - Gap times

KW - Quantile regression

KW - Recurrent events

KW - Within-cluster resampling

UR - http://www.scopus.com/inward/record.url?scp=84879602443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84879602443&partnerID=8YFLogxK

U2 - 10.1111/biom.12010

DO - 10.1111/biom.12010

M3 - Article

C2 - 23489055

AN - SCOPUS:84879602443

VL - 69

SP - 375

EP - 385

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -