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In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
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BOB: Bayesian optimal design for biosimilar trials with co-primary endpoints.

Xiaohan Chi1, Zhangsheng Yu2,3, Ruitao Lin1

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

Statistics in Medicine
|September 21, 2022
PubMed
Summary

This study introduces a novel Bayesian design for biosimilar trials, integrating safety and efficacy endpoints. This approach aims to improve decision-making accuracy and efficiency in evaluating biosimilar drug similarity.

Keywords:
Bayesian optimal designbiosimilarco-primary endpointspowersequential design

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacoeconomics

Background:

  • Biosimilar regulatory approval requires rigorous clinical trials to demonstrate efficacy and safety similarity.
  • Current biosimilar trial designs often monitor efficacy and safety separately, potentially leading to suboptimal trial outcomes.

Purpose of the Study:

  • To propose a Bayesian optimal design for biosimilar trials that unifies safety and efficacy endpoints.
  • To enhance the accuracy and efficiency of decision-making during biosimilar clinical trials.

Main Methods:

  • Development of a Bayesian joint safety and efficacy model.
  • Sequential decision-making using a Bayesian biosimilar probability for go/no-go decisions.
  • Calibration of the design to maximize statistical power while controlling the frequentist type I error rate.

Main Results:

  • The proposed Bayesian design demonstrates desirable performance in simulation studies.
  • The design effectively controls the false positive rate and optimizes average sample size.
  • The methodology was successfully applied to a ranibizumab biosimilar trial.

Conclusions:

  • Integrating safety and efficacy endpoints in a unified Bayesian framework offers a more robust approach to biosimilar trial design.
  • The proposed Bayesian optimal design improves upon traditional methods by allowing for simultaneous evaluation and sequential decision-making.
  • This approach can lead to more reliable regulatory assessments and efficient drug development for biosimilars.