The test-negative design has become popular for evaluating the effectiveness of post-licensure vaccines using observational data. In addition to its logistical convenience on data collection, the design is also believed to control for the differential health-care-seeking behavior between vaccinated and unvaccinated individuals, an important while often unmeasured confounder between the vaccination and infection. Hence, the design has been employed routinely to monitor seasonal flu vaccines and more recently to measure the COVID-19 vaccine effectiveness. Despite its popularity, the design has been questioned, in particular about its ability to fully control for the unmeasured confounding. In this paper, we explore deviations from a perfect test-negative design, and propose various sensitivity analysis methods for estimating the effect of vaccination measured by the causal odds ratio on the subpopulation of individuals with good health-care-seeking behavior. We start with point identification of the causal odds ratio under a test-negative design, comparing different forms of identification assumptions and their corresponding estimands. We then propose two approaches for conducting sensitivity analysis, addressing the influence of the unmeasured confounding in two different ways. Specifically, one approach investigates partial control for unmeasured confounding in the test-negative design, while the other examines the impact of unmeasured confounding on both vaccination and infection. Furthermore, we combine these approaches to provide narrower bounds on the true causal odds ratio, and further sharpen the bounds by restricting the treatment effect heterogeneity. Finally, we apply the proposed methods to evaluate the effectiveness of COVID-19 vaccines using observational data from test-negative designs.

翻译：检验阴性设计已成为利用观察性数据评估疫苗上市后有效性的常用方法。除数据收集的便利性外，该设计被认为能控制疫苗接种与未接种个体间就医行为的差异——这一重要但常未被测量的混杂因素存在于疫苗接种与感染之间。因此，该设计已被常规用于监测季节性流感疫苗，并近期被应用于评估COVID-19疫苗有效性。尽管广泛应用，该设计仍受到质疑，特别是其能否完全控制未测量混杂因素。本文探讨了完美检验阴性设计的偏离情况，提出了多种估算疫苗接种效应的敏感性分析方法（以因果比值比度量），聚焦于具有良好就医行为的亚人群。我们首先在检验阴性设计下比较了因果比值比的逐点识别方法，对比不同识别假设及其对应的估计量；随后提出两种敏感性分析途径，分别从不同角度处理未测量混杂因素的影响：一种途径探讨检验阴性设计对未测量混杂的部分控制能力，另一种则分析未测量混杂对疫苗接种和感染的共同影响。此外，我们通过整合两种方法获得真实因果比值比的更窄界限，并进一步通过限制处理效应异质性来压缩界限范围。最后，我们将所提方法应用于检验阴性设计的观察性数据，评估COVID-19疫苗有效性。