Dealing with positivity violations in mediation analysis via weighted controlled effects, with application to assessing immune correlates of protection in antigen-experienced participants

2026-04-07

AI Summary

This study addresses the challenge posed by high baseline immunity in antigen-experienced populations, which violates the positivity assumption in conventional causal mediation analysis and precludes interventions set below baseline immune levels. To overcome this limitation, the authors propose a weighted controlled risk approach that estimates counterfactual effects among subpopulations defined by a prespecified probability of achieving a given post-vaccination immune marker level, and extend this framework to multi-subgroup comparative analyses. This method represents the first application of a weighting strategy to mitigate positivity violations in antigen-experienced cohorts, thereby broadening the applicability of causal mediation analysis in real-world vaccine research. Simulation studies confirm the validity of the proposed estimator, and the approach is successfully applied in a reanalysis of the COVAIL trial, using Omicron BA.4/BA.5 neutralizing antibody titers as a correlate of SARS-CoV-2 immunity.

Abstract

Causal mediation analysis has become an important and increasingly used framework for evaluating candidate immune response biomarkers in vaccine research. A controlled effects approach has been proposed to estimate controlled risk curves under a counterfactual scenario in which the entire study population is vaccinated and their post-vaccination immune responses are set to a range of fixed levels. This framework performs well when the study population is antigenically naïve, that is, individuals have not been previously exposed to the antigen, as is common in HIV-1 vaccine research and during the early phases of the COVID-19 pandemic. However, the controlled effects framework becomes more challenging to apply in antigen-experienced populations, where prior vaccination or infection has occurred, as in the case of influenza, dengue, and more recent phases of the COVID-19 pandemic. In such settings, a key identification assumption for valid causal mediation analysis, the positivity assumption, is violated: it is no longer plausible to conceive of a hypothetical intervention that sets a post-vaccination immune marker to a fixed level below an individual's baseline immune level. In this article, we introduce a weighted controlled risk approach that targets a subpopulation for whom there is a prespecified probability of attaining a post-vaccination immune marker level. We further generalize this framework to study contrasts of controlled risks for relevant subpopulations. We demonstrate the validity of the proposed estimators through simulation studies and apply the method to reanalyze post-vaccination neutralizing antibody titers against Omicron BA.4/BA.5 as an immune correlate of COVID-19 in the Coronavirus Variant Immunologic Landscape (COVAIL) trial. R code to implement the proposed method can be found on Github: https://github.com/Qijia-He/weighted_CVE.

Problem

• positivity violation
• causal mediation analysis
• antigen-experienced population
• immune correlates of protection
• controlled effects

Innovation

• weighted controlled effects
• positivity violation
• causal mediation analysis
• immune correlates of protection
• antigen-experienced population