  Adaptive subgroup enrichment design is an efficient design framework that
allows accelerated development for investigational treatments while also having
flexibility in population selection within the course of the trial. The
adaptive decision at the interim analysis is commonly made based on the
conditional probability of trial success. However, one of the critical
challenges for such adaptive designs is immature data for interim decisions,
particularly in the targeted subgroup with a limited sample size at the first
stage of the trial. In this paper, we improve the interim decision making by
incorporating information from surrogate endpoints when estimating conditional
power at the interim analysis, by predicting the primary treatment effect based
on the observed surrogate endpoint and prior knowledge or historical data about
the relationship between endpoints. Modified conditional power is developed for
both selecting the patient population to be enrolled after the interim analysis
and sample size re-estimation. In the simulation study, our proposed design
shows a higher chance to make desirable interim decisions and achieves higher
overall power, while controlling the overall type I error. This performance is
robust over drift of prior knowledge from the true relationship between two
endpoints. We also demonstrate the application of our proposed design in two
case studies in oncology and vaccine trials.
