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Parametric and semiparametric copula-based models for the regression analysis of competing risks

Abstract Copula-based constructions of the joint distribution of the overall survival time and the cause-specific failure are developed for the analysis of competing risks data. Covariate effects are incorporated by characterizing the margins with parametric and semiparametric proportional hazards models for the survival times, and a logistic regression model for the cause of failure. Appealing aspects of the methods are the use of single-outcome techniques for the adequate modeling of the proportional hazards component, the ability to find parsimonious models using information-criterion metrics, and the inherent simplicity and usefulness of the interpretation of the parameters. Estimation is carried out using maximum likelihood and two-stage pseudolikelihood procedures for the parametric and semiparametric models, respectively. The performance of the estimators is evaluated in simulation studies. The methods are illustrated with the reanalysis of a follicular cell lymphoma dataset.