Applying the Promising Zone Method to Biomarker-Based Adaptive Designs: A Word of Caution

Sample size re-estimation based on interim data has become an increasingly important component of adaptive clinical trial designs. Many novel anti-cancer therapies under development demonstrate enhanced efficacy in biomarker-defined subpopulations rather than the overall population. To address the inherent uncertainty in the predictive value of biomarkers, biomarker-based adaptive designs have gained considerable interest. Given the complexity of these designs, it is critical to evaluate whether existing sample size re-estimation methods maintain adequate control of the overall Type I error rate. In this paper, we focus on the promising zone method applied within biomarker-based adaptive designs. We show that the promising zone method can produce Type I error inflation when embedded in biomarker-based adaptive designs, with the magnitude of inflation depending on the underlying true treatment effect in the biomarker-positive subgroup and specific design parameters such as the biomarker subgroup prevalence, the interim analysis timing, and the maximum inflation factor. We derive exact analytical results for normally distributed endpoints and extend these findings to time-to-event (TTE) endpoints through the asymptotic normal approximation of the log-rank test statistic, with the conclusions supported by simulation studies. We also discuss regulatory considerations for the implementation of such complex adaptive designs in confirmatory settings.