SC10: Statistical Design and Analysis of Hybrid Controlled Trials with Real-World External Controls

External controls (ECs) from real-world data (RWD) can be used to construct or augment the comparator arm of randomized controlled trials (RCTs) to support and accelerate regulatory and health care decision-making. This has seen increasing use in pediatrics, rare diseases, and diseases with high unmet needs. However, heterogeneity between the RCT and RWD stemming from covariate shift, posterior drift, or unmeasured confounding can lead to biased treatment effect estimates and incorrect decision-making, as noted through several regulatory guidance (FDA 2023, EMA 2023). Nonetheless, carefully designed and appropriately analyzed EC trials can mitigate potential sources of bias and enable robust health care decision-making. This short course will encourage the use, understanding, and acceptability of EC designs in drug development. It will first provide an overview of the statistical framework and challenges in EC trials. A discussion on regulatory perspectives and challenges will be provided. Different data-driven Bayesian and frequentist (using ML) approaches to adaptively borrowing EC subjects while mitigating biases will be presented. Finally, a case study will be presented, along with considerations of the power calculation and sample size determination for EC designs.