A statistical method for evaluating vaccine-induced immune correlates of protection against infection and disease progression: application to the ChAdOx1-S nCoV-19 phase 3 trial
File(s)
Published version
Author(s)
Williams, Lucy R
Voysey, Merryn
Pollard, Andrew J
Grassly, Nicholas C
Type
Journal Article
Abstract
Background: Correlates of protection (CoPs), defined as immune markers statistically correlated with vaccine
efficacy (VE), can be used to accelerate vaccine development. Different components of the immune response may be important for protection against infection and against progression from asymptomatic infection to symptomatic or severe disease. However, CoPs are typically evaluated for these outcomes separately, which can lead to some CoPs not being identified. We propose a novel statistical framework for the integrated evaluation of CoPs for infections with multiple potential outcomes.
Methods: We developed a model of the natural history of an infection that can identify CoPs at each stage of
infection and disease progression and implemented this model in a Bayesian estimation framework. We validated
the model on simulated data then applied it to individual-level clinical and serum neutralising and binding
antibody data from COV002 (NCT04400838), a phase II/III trial of the ChAdOx1 nCoV-19 (AZD1222) vaccine.
We explored logistic and non-parametric (cubic spline) relationships between VE and the candidate CoPs.
Results: Both parametric and non-parametric forms of the model accurately estimated the relationships between the immune CoP and VE against infection (VEin) and against progression to symptoms given infection (VEpr) in 1000 simulated trial datasets. In the COV002 correlates subset (2227 participants, 5315 samples), SARS-CoV-2 spike-specific IgG was positively associated with both VEin and VEpr (average proportion of VE mediated by spike specific IgG, 27 % (95 % CI 2–88 %) for VEin and 41 % (95 % CI 0–96 %) for VEpr). Pseudoneutralisation antibody titres and receptor binding domain (RBD) specific serum IgG showed similar correlations.
Conclusion: Integrated analysis of multiple disease outcomes and candidate CoPs enables the identification of CoPs that operate at different stages of disease progression, which are missed when evaluating outcomes separately.
efficacy (VE), can be used to accelerate vaccine development. Different components of the immune response may be important for protection against infection and against progression from asymptomatic infection to symptomatic or severe disease. However, CoPs are typically evaluated for these outcomes separately, which can lead to some CoPs not being identified. We propose a novel statistical framework for the integrated evaluation of CoPs for infections with multiple potential outcomes.
Methods: We developed a model of the natural history of an infection that can identify CoPs at each stage of
infection and disease progression and implemented this model in a Bayesian estimation framework. We validated
the model on simulated data then applied it to individual-level clinical and serum neutralising and binding
antibody data from COV002 (NCT04400838), a phase II/III trial of the ChAdOx1 nCoV-19 (AZD1222) vaccine.
We explored logistic and non-parametric (cubic spline) relationships between VE and the candidate CoPs.
Results: Both parametric and non-parametric forms of the model accurately estimated the relationships between the immune CoP and VE against infection (VEin) and against progression to symptoms given infection (VEpr) in 1000 simulated trial datasets. In the COV002 correlates subset (2227 participants, 5315 samples), SARS-CoV-2 spike-specific IgG was positively associated with both VEin and VEpr (average proportion of VE mediated by spike specific IgG, 27 % (95 % CI 2–88 %) for VEin and 41 % (95 % CI 0–96 %) for VEpr). Pseudoneutralisation antibody titres and receptor binding domain (RBD) specific serum IgG showed similar correlations.
Conclusion: Integrated analysis of multiple disease outcomes and candidate CoPs enables the identification of CoPs that operate at different stages of disease progression, which are missed when evaluating outcomes separately.
Date Issued
2025-11-20
Date Acceptance
2025-10-09
Citation
Vaccine, 2025, 67
ISSN
0264-410X
Publisher
Elsevier BV
Journal / Book Title
Vaccine
Volume
67
Copyright Statement
© 2025 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Publication Status
Published
Article Number
127856
Date Publish Online
2025-10-15