Estimation of time-dependent association for bivariate failure times in the presence of a competing risk

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Abstract

This article targets the estimation of a time-dependent association measure for bivariate failure times, the conditional cause-specific hazards ratio (CCSHR), which is a generalization of the conditional hazards ratio (CHR) to accommodate competing risks data. We model the CCSHR as a parametric regression function of time and event causes and leave all other aspects of the joint distribution of the failure times unspecified. We develop a pseudo-likelihood estimation procedure for model fitting and inference and establish the asymptotic properties of the estimators. We assess the finite-sample properties of the proposed estimators against the estimators obtained from a moment-based estimating equation approach. Data from the Cache County study on dementia are used to illustrate the proposed methodology.

Original languageEnglish (US)
Pages (from-to)10-20
Number of pages11
JournalBiometrics
Volume70
Issue number1
DOIs
StatePublished - Mar 2014

Keywords

  • Association measure
  • Competing risk
  • Conditional cause-specific hazards ratio
  • Dementia
  • Multivariate survival
  • Pseudo-likelihood

ASJC Scopus subject areas

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

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