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A Workflow for Lipid Nanoparticle (LNP) Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models (SVEM)
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Bayesian adaptive trial design for a newly validated surrogate endpoint.

Lindsay A Renfro1, Bradley P Carlin, Daniel J Sargent

  • 1Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota 55905, USA. renfro.lindsay@mayo.edu

Biometrics
|August 16, 2011
PubMed
Summary

This study introduces a Bayesian adaptive trial design for using validated surrogate endpoints in clinical trials. The novel approach ensures reliable treatment assessment by cautiously incorporating historical data and adaptively switching to primary endpoints if surrogates prove unreliable.

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Regulatory Science

Background:

  • Surrogate endpoints are increasingly used in clinical trials for efficiency, with some gaining regulatory acceptance.
  • Designing trials that prioritize newly validated surrogate endpoints requires careful consideration to ensure reliability.
  • Existing trial designs may not adequately address the cautious integration of surrogate endpoints.

Purpose of the Study:

  • To propose a novel Bayesian adaptive trial design for utilizing validated surrogate endpoints as primary endpoints.
  • To ensure cautious and reliable assessment of intervention effects using surrogate endpoints.
  • To adaptively manage trial progression based on the reliability of the surrogate endpoint.

Main Methods:

  • A Bayesian adaptive trial design incorporating multitrial historical information on surrogate-clinical endpoint relationships.
  • Adaptive evaluation of accumulating trial data against historical relationships to guard against invalid surrogates.
  • Simulation studies comparing the proposed design with the O'Brien-Fleming approach.

Main Results:

  • The proposed design adaptively uses surrogate endpoints, stopping trials early for efficacy, inferiority, or futility.
  • The design can discriminate between trustworthy and untrustworthy surrogate endpoints.
  • Simulations demonstrate favorable operating characteristics compared to standard methods.

Conclusions:

  • The novel Bayesian adaptive design offers a robust framework for employing validated surrogate endpoints in clinical trials.
  • This approach enhances trial efficiency while maintaining a high degree of caution regarding surrogate reliability.
  • The design provides a method for adaptively validating surrogate endpoints within ongoing trials.