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Special issue dedicated to David Oakes.

Lifetime data analysis·2022
Same author

Quantile regression on inactivity time.

Statistical methods in medical research·2021
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Cause-specific quantile regression on inactivity time.

Yichen Jia1, Jong-Hyeon Jeong1

  • 1Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Statistics in Medicine
|January 7, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new quantile regression model for analyzing inactivity time under competing risks, interpreting it as reduced quality of life. The method effectively associates inactivity time with predictors, offering insights for clinical studies.

Keywords:
censored survival datacompeting riskslost lifespanperturbationreversed lifetimetime to event

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Medical Statistics

Background:

  • Traditional time-to-event analyses rely on hazard and survival functions.
  • Competing risks introduce complexities, typically addressed by cause-specific hazard and cumulative incidence functions.
  • Inactivity time, recently revisited, represents life lost and can signify reduced quality of life or post-drug transition periods.

Purpose of the Study:

  • To propose a novel quantile regression model for inactivity time analysis under competing risks.
  • To interpret inactivity time as reduced quality of life and time after drug transition.
  • To associate inactivity time with potential predictors in the presence of competing events.

Main Methods:

  • Definition of the cumulative distribution function for inactivity time specific to event types.
  • Development of a score function-type estimating equation for regression coefficients.
  • Application of a computationally efficient perturbation method for parameter inference due to challenges with probability density functions.

Main Results:

  • The proposed quantile regression model effectively associates inactivity time with predictors under competing risks.
  • Asymptotic properties of regression coefficient estimators are derived.
  • Simulation studies confirm the method's good performance in finite sample settings.
  • The approach simplifies to standard quantile regression when competing events are combined.

Conclusions:

  • The developed method provides a robust framework for analyzing inactivity time in the context of competing risks.
  • It offers a valuable interpretation of inactivity time concerning quality of life and treatment effects.
  • The approach is validated through simulations and demonstrated on a breast cancer dataset.