title: A semiparametric random effects model for multivariate competing risks data.
abstract: We propose a semiparametric random effects model for multivariate competing risks data when the failures of a particular type are of interest. Under this model, the marginal cumulative incidence functions follow a generalized semiparametric additive model. The associations between the cause-specific failure times can be studied through dependence parameters of copula functions that are allowed to depend on cluster-level covariates. A cross-odds ratio-type measure is proposed to describe the associations between cause-specific failure times, and its relationship to the dependence parameters is explored. We develop a two-stage estimation procedure where the marginal models are estimated in the first stage and the dependence parameters are estimated in the second stage. The large sample properties of the proposed estimators are derived. The proposed procedures are applied to Danish twin data to model the cumulative incidence for the age of natural menopause and to investigate the association in the onset of natural menopause between monozygotic and dizygotic twins.
authors.affiliation: Department of Biostatistics , University of Copenhagen , Øster Farimagsgade 5, Copenhagen DK-1014 , Denmark ts@biostat.ku.dk.
authors.forename: Thomas H
authors.initials: TH
authors.lastname: Scheike
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Sun
authors.forename: Mei-Jie
authors.initials: MJ
authors.lastname: Zhang
authors.forename: Tina Kold
authors.initials: TK
authors.lastname: Jensen
keywords: Binomial modelling; Copula function; Cross-odds ratio; Cumulative incidence function; Danish twin data; Estimating equation; Inverse-censoring probability weighting; Two-stage estimation