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Related Experiment Video

Updated: Mar 23, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Comparing biomarkers as trial level general surrogates.

Erin E Gabriel1, Michael J Daniels2,3, M Elizabeth Halloran4,5,6

  • 1Biostatistics Research Branch, Division of Clinical Research, NIAID/NIH, Bethesda, Maryland, U.S.A.

Biometrics
|April 3, 2016
PubMed
Summary

Developing a reliable trial-level general surrogate can reduce vaccine trial costs and approval times. Our new Bayesian method effectively evaluates and compares surrogates, identifying immune measures to predict rotavirus vaccine efficacy in new settings.

Keywords:
Bayesian non-parametricsCross-ValidationMeta-analysisSurrogate markersVaccines

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Vaccinology

Background:

  • Accurate intermediate response measures (surrogates) can streamline clinical trials and speed product licensure.
  • Existing methods for evaluating general surrogates focus on prediction in new settings, not direct comparison of candidates.
  • Few methods formally use cross-validation to quantify prediction error for surrogate evaluation.

Purpose of the Study:

  • To define and propose a method for evaluating and comparing trial-level general surrogates.
  • To estimate absolute prediction error using Bayesian non-parametric modeling and cross-validation.
  • To identify potential immune surrogates for predicting pentavalent rotavirus vaccine efficacy.

Main Methods:

  • Developed a Bayesian non-parametric modeling approach.
  • Incorporated cross-validation to estimate absolute prediction error.
  • Applied the method to evaluate candidate surrogates in multi-national rotavirus vaccine trials.

Main Results:

  • The proposed method performs well across various simulation scenarios.
  • Identified at least one immune measure with potential as a trial-level general surrogate.
  • Successfully used a candidate surrogate to predict efficacy in a trial lacking clinical outcome data.

Conclusions:

  • The developed method provides a robust framework for evaluating and comparing trial-level general surrogates.
  • This approach can reduce the cost and time associated with vaccine development and licensure.
  • Identified immune markers show promise for predicting vaccine efficacy, enabling faster assessment in new populations.