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, which is 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, considering two forms of assumptions on the unmeasured confounder. These assumptions then lead to two approaches for conducting sensitivity analysis, addressing the influence of the unmeasured confounding in different ways. Specifically, one approach investigates partial control for unmeasured confounder in the test-negative design, while the other examines the impact of unmeasured confounder on both vaccination and infection. Furthermore, these approaches can be combined to provide narrower bounds on the true causal odds ratio, and can be further extended to 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：检测阴性设计已成为利用观察数据评估上市后疫苗有效性的常用方法。该设计不仅便于数据收集，还被认为能够控制疫苗接种者与未接种者之间差异化的就医行为——这是疫苗接种与感染之间一个重要但通常未被测量的混杂因素。因此，该设计已常规用于监测季节性流感疫苗，并在近期用于评估COVID-19疫苗的有效性。尽管应用广泛，该设计仍受到质疑，特别是其能否完全控制未测量的混杂因素。本文探讨了检测阴性设计偏离理想条件的情况，并提出了多种敏感性分析方法，用于估计以因果比值比衡量的疫苗接种对具有良好就医行为个体亚群的影响。我们首先在检测阴性设计下考虑未测量混杂因素的两种假设形式，实现了因果比值比的点识别。这些假设进而引出了两种进行敏感性分析的途径，从不同角度处理未测量混杂因素的影响。具体而言，一种途径研究检测阴性设计中未测量混杂因素的部分控制问题，另一种则考察未测量混杂因素对疫苗接种和感染的双重影响。此外，这些方法可以结合使用以缩小真实因果比值比的界限范围，并可通过限制处理效应异质性进一步收紧界限。最后，我们应用所提出的方法，基于检测阴性设计的观察数据评估了COVID-19疫苗的有效性。