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Quantile Regression Adjusting for Dependent Censoring from Semi-Competing Risks.

Ruosha Li1, Limin Peng2

  • 1Ruosha Li, University of Pittsburgh, Pittsburgh, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|January 10, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces new quantile regression methods for event time data with dependent censoring, offering a comprehensive view of covariate effects in biomedical research. The approach handles semi-competing risks, improving analysis when censoring is not independent of the event time.

Keywords:
CopulaDependent censoringQuantile regressionSemi-competing risksStochastic integral equation

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Area of Science:

  • Biostatistics
  • Survival Analysis
  • Statistical Modeling

Background:

  • Event time data often involves censoring, where the exact event time is unknown.
  • Dependent censoring, where censoring is related to the event time, complicates standard survival analysis.
  • Existing quantile regression methods typically assume independent censoring, limiting their applicability in biomedical studies.

Purpose of the Study:

  • To develop novel quantile regression procedures for event time data with potentially dependent censoring.
  • To provide methods for analyzing semi-competing risks data where censoring time is observable post-event.
  • To examine covariate effects on event time and assess the informativeness of censoring under dependent scenarios.

Main Methods:

  • Proposed quantile regression procedures under mild assumptions on the association between event time and censoring time.
  • Developed an efficient and stable algorithm for method implementation.
  • Established asymptotic properties of the estimators, including uniform consistency and weak convergence.

Main Results:

  • The proposed methods provide a comprehensive analysis of covariate effects on event time outcomes.
  • The approach allows for the examination of censoring informativeness in the presence of dependent censoring.
  • Extensive simulations demonstrate good performance with moderate sample sizes.

Conclusions:

  • The developed quantile regression methods effectively address dependent censoring in semi-competing risks settings.
  • The theoretical framework offers a template for similar estimation problems involving stochastic integrals.
  • The method's practical utility is demonstrated through an application to a bone marrow transplant trial.