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Statistical Methods in Medical Research
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Fitting competing risks with an assumed copula

Gabriel Escarela

Departamento de Matematicas, Universidad Autonoma Metropolitana, Unidad Iztapalapa, Mexico DF, Mexico, ge{at}xanum.uam.mx

Jacques F. Carriere

Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada

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.

Statistical Methods in Medical Research, Vol. 12, No. 4, 333-349 (2003)
DOI: 10.1191/0962280203sm335ra
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