A Practical Guide to Optimal Adaptive Two-Stage Designs for Various Endpoint Types

Adaptive designs are frequently used to adjust sample sizes based on interim data, ensuring reliable conclusions while protecting resources. To maximize trial efficiency, design parameters can be optimized to minimize an objective function (e.g., the expected sample size) while rigorously controlling overall error rates or further operating characteristics. However, existing methodological guidance for optimal adaptive designs mainly focuses on normally distributed data or discrete optimizations for binary data, leaving a limited practical framework to practitioners who work with other common endpoint types. We address this by demonstrating how the asymptotic normal approximations of standard test statistics enable the optimization framework to be systematically applied across diverse endpoint types, including binary and time-to-event data. Furthermore, we highlight critical pitfalls in using optimal adaptive designs for two-sided testing. The results demonstrate that optimizing design parameters can substantially decrease expected sample sizes and improve conditional power compared to standard group-sequential designs. Using this guidance, we aim to enable researchers to efficiently plan clinical trials in a broad spectrum of clinical settings, a factor that is especially crucial in rare disease research where only limited patient populations are available.