Unit information prior for incorporating real-world evidence into randomized controlled trials

Randomized controlled trials (RCTs) have been widely recognized as the gold standard to infer the treatment effect in clinical research. Recently, there has been growing interest in enhancing and complementing the result in an RCT by integrating real-world evidence from observational studies. The unit information prior (UIP) is a newly proposed technique that can effectively borrow information from multiple historical datasets. We extend this generic approach to synthesize the non-randomized evidence into a current RCT. Not only does the UIP only require summary statistics published from observational studies for ease of implementation, but it also has clear interpretations and can alleviate the potential bias in the real-world evidence via weighting schemes. Extensive numerical experiments show that the UIP can improve the statistical efficiency in estimating the treatment effect for various types of outcome variables. The practical potential of our UIP approach is further illustrated with a real trial of hydroxychloroquine for treating COVID-19 patients.

Automated summary

Randomized controlled trials (RCTs) are considered the gold standard for clinical research on treatment effects. Recent interest has focused on integrating real-world evidence from observational studies to enhance and complement RCT results. The unit information prior (UIP) is a newly proposed technique that effectively incorporates information from historical datasets. We extend this approach to synthesize non-randomized evidence into current RCTs, improving statistical efficiency in estimating treatment effects for different outcome variables.