Fitting competing risks with an assumed copula.
We propose a fully parametric model for the analysis of competing risks data where the types of failure may not be independent. We show how the dependence between the cause-specific survival times can be modelled with a copula function. Features include: identifiability of the problem; accessible understanding of the dependence structures; and flexibility in choosing marginal survival functions. The model is constructed in such a way that it allows us to adjust for concomitant variables and for a dependence parameter to assess the effects of these on each marginal survival model and on the relationship between the causes of death. The methods are applied to a prostate cancer data set. We find that, with the copula model, more accurate inferences are obtained than with the use of a simpler model such as the independent competing risks approach.
Escarela
Gabriel
G
Departamento de Matemáticas, Universidad Autónoma Metropolitana, Unidad Iztapalapa, México DF, Mexico. ge@xanum.uam.mx
Carrière
Jacques F
JF
D006801:Humans; D008297:Male; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000401:mortality / Q000473:pathology; D018570:Risk Assessment / Q000706:statistics & numerical data*; D016019:Survival Analysis; D017211:Treatment Failure