Test-negative designs are widely used for post-market evaluation of vaccine effectiveness. Different from classical test-negative designs where only healthcare-seekers with symptoms are included, recent test-negative designs have involved individuals with various reasons for testing, especially in an outbreak setting. While including these data can increase sample size and hence improve precision, concerns have been raised about whether they will introduce bias into the current framework of test-negative designs, thereby demanding a formal statistical examination of this modified design. In this article, using statistical derivations, causal graphs, and numerical simulations, we show that the standard odds ratio estimator may be biased if various reasons for testing are not accounted for. To eliminate this bias, we identify three categories of reasons for testing, including symptoms, disease-unrelated reasons, and case contact tracing, and characterize associated statistical properties and estimands. Based on our characterization, we propose stratified estimators that can incorporate multiple reasons for testing to achieve consistent estimation and improve precision by maximizing the use of data. The performance of our proposed method is demonstrated through simulation studies.

翻译：检测阴性设计广泛应用于疫苗上市后有效性评估。不同于经典检测阴性设计仅纳入有症状的求医者，近期研究在疫情暴发等场景中，将因不同原因接受检测的个体纳入此类设计。尽管纳入这些数据能扩大样本量从而提高估计精度，但学界担忧其是否会在现有检测阴性设计框架中引入偏倚，因此亟需对该改良设计进行严格的统计学检验。本文通过统计推导、因果图模型及数值模拟，证明若未对多种检测原因进行控制，标准比值比估计量可能存在偏倚。为消除该偏倚，我们识别出三类检测原因（症状、非疾病相关原因及病例接触追踪），并阐明其对应的统计特性与估计目标。基于上述特征，我们提出分层估计量，通过纳入多重检测原因实现一致估计，并最大限度利用数据以提升精度。通过模拟研究验证了所提方法的性能。