  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.
