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.

翻译：试验-消极设计(TND)已成为一种标准方法,用来评价疫苗在现实世界环境中感染传染病的风险,如流感、罗塔病毒、登盖热和最近的COVID-19。在一项TND研究中,为传染病招募和测试了有症状和寻求护理的人,确定病例和控制。尽管TND有可能减少接种疫苗和未接种的科目在寻求保健行为(HSB)方面未察觉的差异,但仍然受到各种潜在偏见的影响。第一,残留的混淆可能仍然存在,原因是没有观察到HSB、保健工作者的职业或以前感染史。第二,由于选择TD样本是感染和HSB的常见后果,因此,在调整对测试样品的分析时,可能存在相撞分层偏差,从而进一步引起潜伏的HSB的混淆。在这份文件中,我们提出了一个新办法,通过仔细利用一对负控制接触和结果变异因素来确定和估计目标人群的疫苗效果,以便在TND研究中考虑到潜在的隐性偏差。我们提议的Mirgirstal-19疫苗系统的数据模拟和应用,以广泛的方法来说明Mirstirstal-Halvial VI的研究。