title: Estimation of time-dependent association for bivariate failure times in the presence of a competing risk.
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.
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, U.S.A.
authors.forename: Jing
authors.initials: J
authors.lastname: Ning
authors.forename: Karen
authors.initials: K
authors.lastname: Bandeen-Roche
mesh_terms: D017668:Age of Onset; D002648:Child; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003704:Dementia / Q000235:genetics; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D012044:Regression Analysis; D012306:Risk; D014589:Utah
keywords: Association measure; Competing risk; Conditional cause-specific hazards ratio; Dementia; Multivariate survival; Pseudo-likelihood