Critical appraisal of Bayesian dynamic borrowing from an imperfectly commensurate historical control. (17th March 2020)

• Record Type: Journal Article
• Authors: Harun, Nusrat; Liu, Chunyan; Kim, Mi‐Ok
• Is Part Of: Pharmaceutical statistics. Volume 19:Number 5(2020)
• Page Start: 613
• Page End: 625
• Publication Date: 2020-03-17
• Subjects: Bayesian dynamic borrowing designs -- commensurate priors -- doubly adaptive biased coin design -- imperfectly commensurate historical data -- response‐adaptive design; Pharmacy -- Statistical methods -- Periodicals; Pharmacy -- Statistics -- Periodicals; 615.10727
• DOI: 10.1002/pst.2018
• Languages: English
• ISSNs: 1539-1604

Abstract

Summary: Bayesian dynamic borrowing designs facilitate borrowing information from historical studies. Historical data, when perfectly commensurate with current data, have been shown to reduce the trial duration and the sample size, while inflation in the type I error and reduction in the power have been reported, when imperfectly commensurate. These results, however, were obtained without considering that Bayesian designs are calibrated to meet regulatory requirements in practice and even no‐borrowing designs may use information from historical data in the calibration. The implicit borrowing of historical data suggests that imperfectly commensurate historical data may similarly impact no‐borrowing designs negatively. We will provide a fair appraiser of Bayesian dynamic borrowing and no‐borrowing designs. We used a published selective adaptive randomization design and real clinical trial setting and conducted simulation studies under varying degrees of imperfectly commensurate historical control scenarios. The type I error was inflated under the null scenario of no intervention effect, while larger inflation was noted with borrowing. The larger inflation in type I error under the null setting can be offset by the greater probability to stop early correctly under the alternative. Response rates were estimated more precisely and the average sample size was smaller with borrowing. The expected increase in bias with borrowing was noted, but was negligible. Using Bayesian dynamic borrowing designs may improve trial efficiency by stopping trials early correctly and reducing trial length at the small cost of inflated type I error.