There is growing interest in using real-world data (RWD) to complement evidence collected from randomized controlled trials (RCT). This paper discusses study designs that utilize statistical borrowing approaches to augment the control arm of an RCT with a cohort of real-world patients receiving comparable therapy as part of routine care. Such hybrid controlled trials may be particularly appealing in situations where it may be challenging to enroll patients (e.g., rare diseases, concerns about randomizing patients to potentially sub-optimal control arm regimens) or where the trial duration is expected to be long. Compared to using a fully external comparator arm, a hybrid control arm allows for better control of potential biases by preserving randomization in the RCT. Data for borrowing can come from a variety of sources; however RWD sources such as electronic medical records provide the opportunity to borrow information from patients that are more contemporaneous and relevant to the RCT compared to historical clinical trials.
This publication provides an overview of hybrid controlled trials with RWD, discusses data considerations when using RWD especially in the oncology space, and introduces a novel and simple frequentist borrowing method that performs similarly to existing more complex methods.
Why this matters
This methodological work is done against the backdrop of a large medical need. Nearly two million new cancer cases in the United States are projected for 2022 but only a small fraction will enroll in a clinical trial. Hybrid controlled trials leverage the overlap between clinical trial protocols and routine care, using valuable patient resources more efficiently to better meet the high unmet needs of patients with cancer. As with any use of RWD, the data sources need to be carefully assessed on a case-by-case basis to ensure the data are fit-for-purpose, and the operating characteristics of the statistical methods need to be assessed through simulations that mimic the trial at hand. By pairing high-quality RWD with rigorous fit-for-purpose simulations, researchers have the potential to design hybrid controlled trials that better meet the needs of drug development and patients.