Li Kendrick Qijun, Shi Xu, Miao Wang, Tchetgen Eric Tchetgen
Department of Biostatistics, University of Michigan.
Department of Probability and Statistics, Peking University.
ArXiv. 2023 Mar 8:arXiv:2203.12509v4.
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 bias 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 testing, 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 simulation and an application to study COVID-19 vaccine effectiveness using data from the University of Michigan Health System.
检测阴性设计（TND）已成为评估疫苗在现实环境中预防传染病风险有效性的标准方法，如流感、轮状病毒、登革热，以及最近的新冠病毒病。在一项TND研究中，招募出现症状并寻求治疗的个体，并对定义病例和对照的传染病进行检测。尽管TND有潜力减少接种疫苗和未接种疫苗受试者在就医行为（HSB）方面未观察到的差异，但它仍存在各种潜在偏差。首先，由于未观察到的HSB、医护人员职业或既往感染史，可能会残留混杂偏差。其次，由于纳入TND样本是感染和HSB的常见结果，在对检测进行分析时可能存在对撞分层偏差，这会进一步因潜在的HSB导致混杂。在本文中，我们提出了一种新方法，通过精心利用一对阴性对照暴露和结果变量来识别和估计目标人群中的疫苗有效性，以应对TND研究中潜在的隐藏偏差。我们通过广泛的模拟以及使用密歇根大学健康系统的数据研究新冠病毒病疫苗有效性的应用，来说明我们提出的方法。
