Abstract: Competing risks survival data that comprises of more
than one type of event has been used in many applications, and one
of these is in clinical study (e.g. in breast cancer study). The
decision tree method can be extended to competing risks survival
data by modifying the split function so as to accommodate two or
more risks which might be dependent on each other. Recently,
researchers have constructed some decision trees for recurrent
survival time data using frailty and marginal modelling. We further
extended the method for the case of competing risks. In this paper,
we developed the decision tree method for competing risks survival
time data based on proportional hazards for subdistribution of
competing risks. In particular, we grow a tree by using deviance
statistic. The application of breast cancer data is presented. Finally,
to investigate the performance of the proposed method, simulation
studies on identification of true group of observations were executed.