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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\u2010likelihood estimation procedure for model fitting and inference and establish the asymptotic properties of the estimators. We assess the finite\u2010sample properties of the proposed estimators against the estimators obtained from a moment\u2010based estimating equation approach. Data from the Cache County study on dementia are used to illustrate the proposed methodology.<\/jats:p><\/jats:sec>","DOI":"10.1111\/biom.12110","type":"journal-article","created":{"date-parts":[[2013,12,18]],"date-time":"2013-12-18T16:06:50Z","timestamp":1387382810000},"page":"10-20","source":"Crossref","is-referenced-by-count":9,"title":["Estimation of time\u2010dependent association for bivariate failure times in the presence of a competing risk"],"prefix":"10.1093","volume":"70","author":[{"given":"Jing","family":"Ning","sequence":"first","affiliation":[{"name":"Department of Biostatistics The University of Texas MD Anderson Cancer Center, Houston Texas 77030 U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Bandeen\u2010Roche","sequence":"additional","affiliation":[{"name":"Department of Biostatistics Johns Hopkins University, Baltimore Maryland 21025 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