  Time to an event of interest over a lifetime is a central measure of the
clinical benefit of an intervention used in a health technology assessment
(HTA). Within the same trial multiple end-points may also be considered. For
example, overall and progression-free survival time for different drugs in
oncology studies. A common challenge is when an intervention is only effective
for some proportion of the population who are not clinically identifiable.
Therefore, latent group membership as well as separate survival models for
groups identified need to be estimated. However, follow-up in trials may be
relatively short leading to substantial censoring. We present a general
Bayesian hierarchical framework that can handle this complexity by exploiting
the similarity of cure fractions between end-points; accounting for the
correlation between them and improving the extrapolation beyond the observed
data. Assuming exchangeability between cure fractions facilitates the borrowing
of information between end-points. We show the benefits of using our approach
with a motivating example, the CheckMate 067 phase 3 trial consisting of
patients with metastatic melanoma treated with first line therapy.
