BOB: Bayesian optimal design for biosimilar trials with co‐primary endpoints. (20th September 2022)

• Record Type: Journal Article
• Authors: Chi, Xiaohan; Yu, Zhangsheng; Lin, Ruitao
• Is Part Of: Statistics in medicine. Volume 41:Number 26(2022)
• Page Start: 5319
• Page End: 5334
• Publication Date: 2022-09-20
• Subjects: Bayesian optimal design -- biosimilar -- co‐primary endpoints -- power -- sequential design; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9571
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : For regulatory approval of a biosimilar product, extensive evaluations should be performed by rigorous clinical trials to establish the similarity between the reference product and the proposed biosimilar in terms of both efficacy and safety. Existing designs for biosimilar trials often use a single primary efficacy endpoint in trial monitoring, and then separately evaluate the safety of the biosimilar product in a secondary analysis at the trial completion. However, ignoring the safety endpoint and the correlation between safety and efficacy in trial monitoring may lead to a high false positive rate, or it may delay the termination of the trial when dissimilarity in safety is early detected. We propose a Bayesian optimal design for biosimilar trials by incorporating both safety and efficacy endpoints in a unified framework. Based on a Bayesian joint safety and efficacy model, we sequentially use a so‐called Bayesian biosimilar probability to make go/no‐go decisions. We calibrate the Bayesian design to maximize the statistical power while maintaining the frequentist type I error rate at the nominal level. We carry out extensive simulation studies to show that the design has desirable performance in terms of the false positive rate and the average sample size. We also apply the proposed design to a biosimilar trial evaluating a ranibizumab product.