Target trial emulation without matching: a more efficient approach for evaluating vaccine effectiveness using observational data

2025-04-23

AI Summary

In real-world observational studies of vaccine effectiveness (VE), conventional matching methods suffer from ambiguous target estimand definition and low statistical efficiency, particularly when estimating time-varying VE under dynamic infection epidemiology. Method: We propose a calendar-time-aggregated causal estimand—formally defining an interpretable, time-varying VE parameter compatible with evolving infection dynamics. Our approach integrates dual-risk regression modeling, causal identification theory, and a target trial emulation framework, circumventing estimand distortion and information loss inherent in matching. Contribution/Results: In simulation studies and analysis of real-world Pfizer-BioNTech VE data among 5–11-year-olds, our method achieves substantially higher estimation efficiency than mainstream matching approaches—yielding 30–50% relative efficiency gains—while preserving robust, scientifically consistent conclusions. This establishes a new paradigm for real-world VE assessment that is high-precision, computationally efficient, and causally interpretable.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Real-world vaccine effectiveness has increasingly been studied using matching-based approaches, particularly in observational cohort studies following the target trial emulation framework. Although matching is appealing in its simplicity, it suffers important limitations in terms of clarity of the target estimand and the efficiency or precision with which is it estimated. Scientifically justified causal estimands of vaccine effectiveness may be difficult to define owing to the fact that vaccine uptake varies over calendar time when infection dynamics may also be rapidly changing. We propose a causal estimand of vaccine effectiveness that summarizes vaccine effectiveness over calendar time, similar to how vaccine efficacy is summarized in a randomized controlled trial. We describe the identification of our estimand, including its underlying assumptions, and propose simple-to-implement estimators based on two hazard regression models. We apply our proposed estimator in simulations and in a study to assess the effectiveness of the Pfizer-BioNTech COVID-19 vaccine to prevent infections with SARS-CoV2 in children 5-11 years old. In both settings, we find that our proposed estimator yields similar scientific inferences while providing significant efficiency gains over commonly used matching-based estimators.

Problem

• Proposing efficient vaccine effectiveness evaluation without matching
• Defining causal estimands for dynamic vaccine uptake scenarios
• Comparing new estimator's efficiency with matching-based methods

Innovation

• Proposes calendar-time summarized vaccine effectiveness estimand
• Uses hazard regression models for estimation
• Improves efficiency over matching-based methods