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

TL;DR

This paper introduces new statistical methods to estimate cumulative incidence curves by combining data from vaccine efficacy trials and immunobridging studies, especially for pathogens with multiple serotypes, and demonstrates their application to COVID-19 booster data.

Contribution

It develops efficient, robust estimators for counterfactual cumulative incidence curves using participant-level data from multiple sources, extending to multi-serotype pathogens.

Findings

Proposed estimators perform well in finite samples through simulation.

Applied methods to estimate COVID-19 booster efficacy and test causal assumptions.

Extended methods to handle multiple serotypes like dengue and influenza.

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.