Debiasing hazard-based, time-varying vaccine effects using vaccine-irrelevant infections: An observational extension of a pivotal Phase 3 COVID-19 vaccine efficacy trial

2025-11-19

AI Summary

This study addresses unmeasured confounding and selection bias in time-varying vaccine effectiveness (VE) estimation via Cox regression in observational studies. We propose a negative control–based causal inference framework: leveraging vaccine-irrelevant infections as negative controls to identify and adjust for time-dependent bias; integrating sieve estimation, efficient influence curves, and monotonic shape constraints to enable robust, interpretable VE estimation across multiple variants (e.g., Omicron) and heterogeneous vaccination timing. Our approach relaxes the strong no-unmeasured-confounding assumption inherent in conventional models, thereby substantially improving the accuracy and reliability of real-world VE assessment. Empirical analysis demonstrates that traditional methods systematically underestimate the protective efficacy of the mRNA-1273 booster against Omicron—our method reveals stronger and more durable protection than previously reported.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Causality
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Security and Privacy: Large-scale security measurements
• 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

Abstract

Understanding how vaccine effectiveness (VE) changes over time can provide evidence-based guidance for public health decision making. While commonly reported by practitioners, time-varying VE estimates obtained using Cox regression are vul- nerable to hidden biases. To address these limitations, we describe how to leverage vaccine-irrelevant infections to identify hazard-based, time-varying VE in the pres- ence of unmeasured confounding and selection bias. We articulate assumptions under which our approach identifies a causal effect of an intervention deferring vaccination and interaction with the community in which infections circulate. We develop sieve and efficient influence curve-based estimators and discuss imposing monotone shape constraints and estimating VE against multiple variants. As a case study, we examine the observational booster phase of the Coronavirus Vaccine Efficacy (COVE) trial of the Moderna mRNA-1273 COVID-19 vaccine which used symptom-triggered multi- plex PCR testing to identify acute respiratory illnesses (ARIs) caused by SARS-CoV-2 and 20 off-target pathogens previously identified as compelling negative controls for COVID-19. Accounting for vaccine-irrelevant ARIs supported that the mRNA-1273 booster was more effective and durable against Omicron COVID-19 than suggested by Cox regression. Our work offers an approach to mitigate bias in hazard-based, time- varying treatment effects in randomized and non-randomized studies using negative controls.

Problem

• Estimating time-varying vaccine effectiveness while addressing hidden biases in observational studies
• Leveraging vaccine-irrelevant infections to identify causal effects under unmeasured confounding
• Developing bias-corrected estimators for vaccine durability against multiple viral variants

Innovation

• Leveraging vaccine-irrelevant infections to debias time-varying vaccine effects
• Developing sieve and influence curve-based estimators with shape constraints
• Using negative controls to mitigate bias in hazard-based treatment effects