Double Negative Control Inference in Test-Negative Design Studies of Vaccine Effectiveness
针对检测阴性设计（TND）中因未观测的医疗寻求行为等导致的混杂偏倚，提出利用一对阴性对照暴露和结局变量来识别和估计疫苗有效性的新方法，并通过模拟和COVID-19数据验证。
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.