  In this article, a general family of bivariate distributions is used to model
competing risks data with dependent factors. The general structure of competing
risks data considered here includes ties. A comprehensive inferential framework
for the proposed model is presented: maximum likelihood estimation, confidence
interval construction, and model selection within the bivariate family of
distributions for a given dependent competing risks data. The inferential
methods are very convenient to implement. Through detailed simulations, the
inferential methods are observed to provide quite reasonable results. Analysis
of a real data from the Diabetic Retinopathy Study is carried out with the help
of the proposed model as an illustrative example.
