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Estimation of time‐dependent association for bivariate failure times in the presence of a competing risk
Author(s) -
Ning Jing,
BandeenRoche Karen
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12110
Subject(s) - bivariate analysis , estimator , statistics , econometrics , inference , mathematics , estimating equations , generalization , moment (physics) , joint probability distribution , conditional probability distribution , parametric statistics , computer science , mathematical analysis , physics , classical mechanics , artificial intelligence
Summary 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.