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

BACKGROUND: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.