External control arms are moving from theory to practice as drug developers begin to use them to make internal go/no-go decisions for clinical programs and to support regulatory applications. The field is largely split between those drawing on past clinical trials versus real-world data, and at least one company is pushing the approach further by simulating artificial patients to augment control arms in complex, chronic indications like Alzheimer’s disease.
Randomizing patients into experimental and control arms is critical to building confidence that the benefits observed in patients treated with a drug are due to the drug itself, and not the patients’ baseline characteristics. But the prospect of being assigned to a control group leads many patients to opt out of the clinical trial process all together, and filling control groups in rare and severe diseases is often infeasible or unethical.
External control arms offer a way to reduce the number of study participants treated with placebo or standard of care, decreasing trial size, duration and cost and incentivizing patient participation.
The idea is to replicate traditional randomization using control data from past clinical trials or real-world data (RWD) from electronic health records (EHRs) and other sources. RWD goes beyond natural history data because it can include patients undergoing treatment in real time and provides more detailed information at the level of individual patients.
At a Friends of Cancer Research meeting in November,