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

COVID-19 vaccine Causal inference Coronavirus prevention network, COVE vaccine efficacy trial Mediation Principal stratification Surrogate endpoint

Journal

Vaccine
ISSN: 1873-2518
Titre abrégé: Vaccine
Pays: Netherlands
ID NLM: 8406899

Informations de publication

Date de publication:
07 Mar 2024
Historique:
received: 12 12 2023
revised: 22 02 2024
accepted: 23 02 2024
medline: 9 3 2024
pubmed: 9 3 2024
entrez: 8 3 2024
Statut: aheadofprint

Résumé

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.

Identifiants

pubmed: 38458870
pii: S0264-410X(24)00247-0
doi: 10.1016/j.vaccine.2024.02.071
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIAID NIH HHS
ID : UM1 AI068635
Pays : United States

Informations de copyright

Copyright © 2024 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Peter B Gilbert (PB)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA. Electronic address: pgilbert@fredhutch.org.

Youyi Fong (Y)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.

Nima S Hejazi (NS)

Department of Biostatistics, T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.

Avi Kenny (A)

Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.

Ying Huang (Y)

Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.

Marco Carone (M)

Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA.

David Benkeser (D)

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.

Dean Follmann (D)

Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rockville, MD, USA.

Classifications MeSH