Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness

TL;DR

This paper introduces a novel negative control approach to address hidden biases in test-negative design studies for vaccine effectiveness, improving bias correction and estimate accuracy.

Contribution

It develops a new method using negative control variables to identify and estimate vaccine effectiveness despite unobserved confounding and selection biases.

Findings

Method effectively reduces bias in simulations

Application yields more accurate COVID-19 vaccine effectiveness estimates

Demonstrates robustness against unobserved confounders

Abstract

The test-negative design (TND) has become a standard approach to evaluate vaccine effectiveness against the risk of acquiring infectious diseases in real-world settings, such as Influenza, Rotavirus, Dengue fever, and more recently COVID-19. In a TND study, individuals who experience symptoms and seek care are recruited and tested for the infectious disease which defines cases and controls. Despite TND's potential to reduce unobserved differences in healthcare seeking behavior (HSB) between vaccinated and unvaccinated subjects, it remains subject to various potential biases. First, residual confounding may remain due to unobserved HSB, occupation as healthcare worker, or previous infection history. Second, because selection into the TND sample is a common consequence of infection and HSB, collider stratification bias may exist when conditioning the analysis on tested samples, which further induces confounding by latent HSB. In this paper, we present a novel approach to identify and estimate vaccine effectiveness in the target population by carefully leveraging a pair of negative control exposure and outcome variables to account for potential hidden bias in TND studies. We illustrate our proposed method with extensive simulations and an application to study COVID-19 vaccine effectiveness using data from the University of Michigan Health System.