Sequential Adaptive Design Method for Incorporating External Data

External data (e.g., real-world data (RWD) and historical data) have become more readily available. This has led to rapidly increasing interest in exploring and evaluating ways of utilizing external data to facilitate traditional clinical trials (TCT), especially for rare diseases with high unmet medical needs where a TCT would be impractical and/or unethical. In this article, we focus on hybrid studies that incorporate external data into randomized clinical trials to augment the control arm and explore a complex innovative design. A sequential adaptive design conducts multiple interim assessments to improve the accuracy of estimates of agreement between external data and current data. At each interim assessment, we apply the inverse probability weighted power prior (IPW-PP) method to adaptively borrow information from external data to account for confounding and heterogeneity. The randomization ratio is dynamically adjusted during the interim assessment based on accumulatively augmented information to reduce the sample size of the current trial. Additionally, the proposed design can be extended to allow interim analyses for early efficacy/futility stopping, that is, early assessment of trial success or failure based on accumulated data, potentially reducing ineffective treatment exposure and unnecessary time and resources. The performance of the proposed method and design is evaluated via extensive simulation studies. The sequential adaptive design and IPW-PP approach having desirable properties are implemented.