A0866
Title: Double negative control inference in test-negative design studies of vaccine effectiveness
Authors: Kendrick Li - University of Michigan (United States) [presenting]
Xu Shi - University of Michigan (United States)
Wang Miao - Peking University (China)
Eric Tchetgen Tchetgen - The Wharton School, University of Pennsylvania (United States)
Abstract: The test-negative design (TND) has become a standard approach to evaluating 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 bias may remain due to unobserved HSB, occupation as a 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 testing, which further induces confounding by latent HSB. We present a novel approach to identifying and estimating 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 simulation and an application to study COVID-19 vaccine effectiveness using data from the University of Michigan Health System.
