BOB: Bayesian Optimal Design for Biosimilar Trials With Co-Primary Endpoints

Authors

Xiaohan Chi
Zhangsheng Yu
Ruitao Lin

Publication Date

11-20-2022

Journal

Statistics in Medicine

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.

Keywords

Humans, Bayes Theorem, Biosimilar Pharmaceuticals, Probability, Ranibizumab, Research Design, Sample Size, Clinical Trials as Topic, Bayesian optimal design, Biosimilar, Co-primary endpoints, Power, Sequential design

DOI

10.1002/sim.9571

PMID

36127794

PMCID

PMC9588749

PubMedCentral® Posted Date

11-20-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes