  We consider event-driven clinical trials, where the analysis is performed
once a pre-determined number of clinical events has been reached. For example,
these events could be progression in oncology or a stroke in cardiovascular
trials. At the interim stage, one of the main tasks is predicting the number of
events over time and the time to reach specific milestones, where we need to
account for events that may occur not only in patients already recruited and
are followed-up but also in patients yet to be recruited. Therefore, in such
trials we need to model patient recruitment and event counts together. In the
paper we develop a new analytic approach which accounts for the opportunity of
patients to be cured, as well as for them to dropout and be lost to follow-up.
Recruitment is modelled using a Poisson-gamma model developed in previous
publications. When considering the occurrence of events, we assume that the
time to the main event and the time to dropout are independent random
variables, and we have developed a few advanced models with cure using
exponential, Weibull and log-normal distributions. This technique is supported
by well developed, tested and documented software. The results are illustrated
using simulation and a real dataset with reference to the developed software.
