  An assurance calculation is a Bayesian alternative to a power calculation.
One may be performed to aid the planning of a clinical trial, specifically
setting the sample size or to support decisions about whether or not to perform
a study. Immuno-oncology is a rapidly evolving area in the development of
anticancer drugs. A common phenomenon that arises in trials of such drugs is
one of delayed treatment effects, that is, there is a delay in the separation
of the survival curves. To calculate assurance for a trial in which a delayed
treatment effect is likely to be present, uncertainty about key parameters
needs to be considered. If uncertainty is not considered, the number of
patients recruited may not be enough to ensure we have adequate statistical
power to detect a clinically relevant treatment effect and the risk of an
unsuccessful trial is increased. We present a new elicitation technique for
when a delayed treatment effect is likely and show how to compute assurance
using these elicited prior distributions. We provide an example to illustrate
how this can be used in practice and develop open-source software to implement
our methods. Our methodology has the potential to improve the success rate and
efficiency of Phase III trials in immuno-oncology and for other treatments
where a delayed treatment effect is expected to occur.
