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 language | English (US) |
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Pages (from-to) | 10-20 |
Number of pages | 11 |
Journal | Biometrics |
Volume | 70 |
Issue number | 1 |
DOIs | |
State | Published - 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