Efficient estimation of cumulative incidence curves via data fusion with surrogates: application to integrated analysis of vaccine trial and immunobridging data

2026-04-14

AI Summary

This study addresses the challenge of evaluating updated vaccine regimens against pathogens with multiple serotypes by proposing a causal inference framework that integrates individual-level data from historical phase III vaccine trials and immunobridging studies to estimate counterfactual and etiology-specific cumulative incidence curves. Methodologically, the authors develop a multiply robust, efficient estimator capable of testing the no-controlled direct effect assumption and validate its finite-sample performance through simulations. Applied to data from the COVAIL trial, the approach successfully reconstructs hypothetical cumulative incidence curves for bivalent mRNA booster vaccines, corroborating the plausibility of key causal assumptions and offering a novel tool for assessing the effectiveness of variant-matched vaccines.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Refined vaccine regimens containing variant-matched inserts are often authorized based on historical phase 3 efficacy trials together with immunobridging studies. Phase 3 trials are essential for establishing immune biomarkers that reliably predict disease risk or vaccine efficacy against clinical endpoints. Once such immune correlates are identified, updated vaccine regimens can be approved through immunobridging designs that compare the immunogenicity of the updated regimen to that of an already-approved vaccine. We develop methods of inference for the counterfactual cumulative incidence curve using participant-level data from both a historical vaccine efficacy trial and an immunobridging study. We further extend these methods to pathogens with multiple serotypes -- such as dengue virus and influenza -- by estimating cause-specific cumulative incidence curves. We describe the identification assumptions, propose efficient and multiply robust estimators, and assess their finite-sample performance through simulation studies. We then apply the proposed methods to (1) estimating the hypothetical cumulative incidence curve for a bivalent mRNA booster and (2) testing a key assumption of no controlled direct effects, using data from the COVID-19 Variant Immunologic Landscape (COVAIL) Trial, a multistage randomized clinical study evaluating the safety and immunogenicity of a second COVID-19 booster dose.

Problem

• cumulative incidence
• data fusion
• immunobridging
• vaccine efficacy
• counterfactual estimation

Innovation

• data fusion
• cumulative incidence curve
• immunobridging
• multiply robust estimation
• counterfactual inference