Assessing COVID-19 Vaccine Effectiveness in Observational Studies via Nested Trial Emulation (preprint)

2024-03-26

AI Summary

In real-world observational data, vaccine effectiveness estimation suffers from selection bias, immortal time bias, and confounding—particularly under viral evolution inducing time-varying treatment effects. To address these challenges, we propose a time-varying inverse probability weighting estimator within the nested trial emulation (NTE) framework. This is the first method enabling identification and estimation of time-varying causal effects, while supporting formal testing of effect homogeneity—thereby overcoming interpretational limitations of conventional static summary estimators. Evaluated on longitudinal electronic health records from over 120,000 residents in Abruzzo, Italy, the estimator demonstrates robust finite-sample performance, substantially mitigates multiple biases, and precisely characterizes heterogeneous vaccine effectiveness trajectories across both calendar time and time-since-vaccination.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Observational data are often used to estimate real-world effectiveness and durability of coronavirus disease 2019 (COVID-19) vaccines. A sequence of nested trials can be emulated to draw inference from such data while minimizing selection bias, immortal time bias, and confounding. Typically, when nested trial emulation (NTE) is employed, effect estimates are pooled across trials to increase statistical efficiency. However, such pooled estimates may lack a clear interpretation when the treatment effect is heterogeneous across trials. In the context of COVID-19, vaccine effectiveness quite plausibly will vary over calendar time due to newly emerging variants of the virus. This manuscript considers a NTE inverse probability weighted estimator of vaccine effectiveness that may vary over calendar time, time since vaccination, or both. Statistical testing of the trial effect homogeneity assumption is considered. Simulation studies are presented examining the finite-sample performance of these methods under a variety of scenarios. The methods are used to estimate vaccine effectiveness against COVID-19 outcomes using observational data on over 120,000 residents of Abruzzo, Italy during 2021.

Problem

• Estimating time-varying vaccine effectiveness from observational data
• Addressing selection bias and confounding in observational studies
• Testing vaccine effect homogeneity across different population subgroups

Innovation

• Nested trial emulation for observational vaccine studies
• Inverse probability weighted estimator for time-varying effectiveness
• Standardization of trial-specific inferences to reduce bias