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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Generalizing Quantile Regression for Counting Processes with Applications to Recurrent Events.

Xiaoyan Sun1, Limin Peng2, Yijian Huang3

  • 1Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322 ( xsun33@emory.edu ).

Journal of the American Statistical Association
|May 24, 2016
PubMed
Summary
This summary is machine-generated.

This study extends quantile regression to model recurrent events data, offering a flexible framework for survival analysis. The new model maintains interpretability and accommodates complex observational data, enhancing statistical modeling capabilities.

Keywords:
accelerated failure time modelaccelerated recurrence time modelcensored quantile regressioncounting processrecurrent eventsvarying covariate effects

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Quantile regression is valuable for analyzing covariate effects on event time distributions.
  • Existing methods may not fully address recurrent events or complex observational data.

Purpose of the Study:

  • To extend quantile regression for modeling counting processes, specifically for recurrent events data.
  • To develop a unified theoretical and inferential framework for this new model.
  • To propose an alternative covariance estimation procedure.

Main Methods:

  • Developed a novel counting process model based on quantile regression principles.
  • Unified the new model with existing censored quantile regression methods.
  • Proposed a sample-based covariance estimation procedure.

Main Results:

  • The proposed recurrent events model retains quantile regression's interpretability and flexibility.
  • The framework accommodates diverse observational schemes.
  • The sample-based covariance estimator complements bootstrapping.

Conclusions:

  • The extended quantile regression framework provides a robust approach for recurrent events survival analysis.
  • The methodology is applicable to complex observational studies.
  • The proposed methods were validated through simulations and a real-world dataset.