Incorporating Surrogate Information for Adaptive Subgroup Enrichment Design with Sample Size Re-Estimation

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