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Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial.

Publication ,  Journal Article
Gilbert, PB; Fong, Y; Hejazi, NS; Kenny, A; Huang, Y; Carone, M; Benkeser, D; Follmann, D
Published in: Vaccine
April 2, 2024

A central goal of vaccine research is to characterize and validate immune correlates of protection (CoPs). In addition to helping elucidate immunological mechanisms, a CoP can serve as a valid surrogate endpoint for an infectious disease clinical outcome and thus qualifies as a primary endpoint for vaccine authorization or approval without requiring resource-intensive randomized, controlled phase 3 trials. Yet, it is challenging to persuasively validate a CoP, because a prognostic immune marker can fail as a reliable basis for predicting/inferring the level of vaccine efficacy against a clinical outcome, and because the statistical analysis of phase 3 trials only has limited capacity to disentangle association from cause. Moreover, the multitude of statistical methods garnered for CoP evaluation in phase 3 trials renders the comparison, interpretation, and synthesis of CoP results challenging. Toward promoting broader harmonization and standardization of CoP evaluation, this article summarizes four complementary statistical frameworks for evaluating CoPs in a phase 3 trial, focusing on the frameworks' distinct scientific objectives as measured and communicated by distinct causal vaccine efficacy parameters. Advantages and disadvantages of the frameworks are considered, dependent on phase 3 trial context, and perspectives are offered on how the frameworks can be applied and their results synthesized.

Duke Scholars

Published In

Vaccine

DOI

EISSN

1873-2518

Publication Date

April 2, 2024

Volume

42

Issue

9

Start / End Page

2181 / 2190

Location

Netherlands

Related Subject Headings

  • Virology
  • Vaccines
  • Vaccine Efficacy
  • Research Design
  • Randomized Controlled Trials as Topic
  • Causality
  • Biomarkers
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 11 Medical and Health Sciences
 

Citation

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Gilbert, P. B., Fong, Y., Hejazi, N. S., Kenny, A., Huang, Y., Carone, M., … Follmann, D. (2024). Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial. Vaccine, 42(9), 2181–2190. https://doi.org/10.1016/j.vaccine.2024.02.071
Gilbert, Peter B., Youyi Fong, Nima S. Hejazi, Avi Kenny, Ying Huang, Marco Carone, David Benkeser, and Dean Follmann. “Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial.Vaccine 42, no. 9 (April 2, 2024): 2181–90. https://doi.org/10.1016/j.vaccine.2024.02.071.
Gilbert PB, Fong Y, Hejazi NS, Kenny A, Huang Y, Carone M, et al. Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial. Vaccine. 2024 Apr 2;42(9):2181–90.
Gilbert, Peter B., et al. “Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial.Vaccine, vol. 42, no. 9, Apr. 2024, pp. 2181–90. Pubmed, doi:10.1016/j.vaccine.2024.02.071.
Gilbert PB, Fong Y, Hejazi NS, Kenny A, Huang Y, Carone M, Benkeser D, Follmann D. Four statistical frameworks for assessing an immune correlate of protection (surrogate endpoint) from a randomized, controlled, vaccine efficacy trial. Vaccine. 2024 Apr 2;42(9):2181–2190.
Journal cover image

Published In

Vaccine

DOI

EISSN

1873-2518

Publication Date

April 2, 2024

Volume

42

Issue

9

Start / End Page

2181 / 2190

Location

Netherlands

Related Subject Headings

  • Virology
  • Vaccines
  • Vaccine Efficacy
  • Research Design
  • Randomized Controlled Trials as Topic
  • Causality
  • Biomarkers
  • 42 Health sciences
  • 32 Biomedical and clinical sciences
  • 11 Medical and Health Sciences