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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Quantile regression for recurrent gap time data.

Xianghua Luo1, Chiung-Yu Huang, Lan Wang

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA. luox0054@umn.edu

Biometrics
|March 16, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantile regression method for analyzing recurrent gap times, offering direct interpretation of covariate effects. The method extends existing techniques for survival data and proves effective in medical research.

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Area of Science:

  • Biostatistics
  • Medical Statistics
  • Epidemiology

Background:

  • Analyzing recurrent gap times is crucial in medical and public health research.
  • Existing methods often model hazard functions, limiting direct interpretation of covariate effects on gap times.
  • Quantile regression offers a direct approach to assess covariate effects on the distribution of gap times.

Purpose of the Study:

  • To develop a novel method for analyzing recurrent gap time data using quantile regression.
  • To provide direct assessment of covariate effects on the quantiles of recurrent gap times.
  • To extend martingale-based estimating equations for univariate survival data to recurrent gap time analysis.

Main Methods:

  • Utilizing quantile regression for direct assessment of covariate effects.
  • Extending martingale-based estimating equation methods for recurrent gap time data.
  • Adapting the weighted risk-set method for recurrent event analysis.

Main Results:

  • The proposed estimation procedure is compatible with existing univariate censored quantile regression software.
  • Theoretical properties, including uniform consistency and weak convergence, of the estimators are established.
  • Monte Carlo simulations confirm the method's effectiveness.

Conclusions:

  • The developed quantile regression approach provides a valuable tool for analyzing recurrent gap times.
  • This method allows for direct interpretation of covariate effects, enhancing clinical and public health insights.
  • The approach is demonstrated through an application to psychiatric register data.