Matching-assisted power prior for incorporating real-world data in randomized clinical trial

Music City Center 

Leveraging external data to supplement randomized clinical trials has become increasingly popular, particularly for medical device and drug discovery. In rare diseases, recruiting enough patients for large-scale trials is challenging. To address this, small hybrid trials can borrow historical controls or real-world data (RWD) to increase statistical power, but borrowing must follow a statistically principled manner. This paper proposes a matching-assisted power prior method to mitigate bias when incorporating external data. Using template matching, a subset of comparable external subjects is grouped and assigned weights based on their similarity to the current study population. These weighted groups are then integrated into Bayesian inference through power priors. Unlike traditional power prior methods, which apply similar discounts to all control patients, our approach pre-selects high-quality controls, improving the reliability of borrowed data. Through simulation studies, we compare its performance with the propensity score-integrated power prior approach. Finally, we demonstrate its practical implementation using data from a real acupuncture clinical trial.

Bayesian dynamic borrowing

power prior

template matching

propensity score

real-world data 

Biopharmaceutical Section