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In this paper, the structure of semicompeting risks data, dened by Fine, Jiang & Chappell (2001), is studied. Two events are of interest: a nonterminal and a terminal event, the last one, can censor the non-terminal event, but not vice versa. Due to the possible dependence between the times until the occurrence of such events, two approaches are evaluated: modelling the bivariate survival function through Archimedean copulas and a shared frailty model. A simulation is conducted to examine its performance and both approaches are applied to a real data set of patients with chronic kidney disease (CKD).

Modeling Data With Semicompeting Risks: An Application to Chronic Kidney Disease in Colombia