BIRTH: Bayesian integration of real-world data for trials with hybrid arms

Basket trials accelerate the evaluation of targeted therapies across molecularly defined disease subtypes, but establishing reliable control groups is challenging. Historical controls risk bias, while 1:1 randomized controls are often infeasible in rare disease settings due to substantial costs and recruitment difficulties. This study aimed to develop a novel Bayesian trial design to address these challenges by efficiently integrating real-world data. We propose the BIRTH (Bayesian Integration of Real-world data for Trials with Hybrid arms) design, a statistical framework for randomized basket trials. The methodology integrates external real-world data (RWD) using a dual-layer, dynamic borrowing architecture. It employs propensity score weighting to mitigate confounding and hierarchical Bayesian models to dynamically regulate borrowing. A key innovation is an asymmetric borrowing strategy: the control arm borrows from both RWD and internal subgroups, while the treatment arm borrows exclusively from internal trial arms, quarantining it from RWD-induced bias. The design’s operating characteristics were evaluated via extensive simulation studies. When RWD was concordant, the BIRTH design significantly reduced bias and mean squared error while substantially improving statistical power. While the design maintained robust type I error control in many scenarios, inflation was possible under extreme heterogeneity. Notably, BIRTH achieved statistical power equivalent to a 1:1 randomized trial but with a 2:1 randomization ratio, reducing the required control group sample size by 50%. The BIRTH design provides a rigorous and efficient solution for designing basket trials in oncology and rare diseases. By leveraging multi-source data through a novel asymmetric borrowing strategy, it can make randomized trials more feasible where control arm recruitment is a barrier.