Test-negative designs are widely used for post-market evaluation of vaccine effectiveness, particularly in cases where randomization is not feasible. Differing 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 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 show how to consistently estimate each estimand via stratification. Furthermore, we describe when these estimands correspond to the same vaccine effectiveness parameter, and, when appropriate, propose a stratified estimator that can incorporate multiple reasons for testing and improve precision. The performance of our proposed method is demonstrated through simulation studies.

翻译：检测阴性设计被广泛用于疫苗效力的上市后评估，尤其是在随机化不可行的情况下。与经典检测阴性设计仅纳入有症状的求医者不同，近年来特别是在疫情暴发背景下，该设计纳入了因各种原因接受检测的个体。虽然纳入这些数据可增加样本量从而提高精度，但人们担忧其是否会引入偏差至当前检测阴性设计框架，因此需要对该改良设计进行严谨的统计学检验。本文通过统计推导、因果图模型及数值模拟证明：若未考虑不同检测原因，标准比值比估计量可能存在偏差。为消除该偏差，我们明确了检测的三大类原因——症状、非疾病相关因素及病例接触追踪——并刻画了其统计特性及估计目标。基于上述刻画，我们展示了如何通过分层对每个估计目标进行一致估计。进一步地，我们描述了这些估计目标对应相同疫苗效力参数的条件，并在适用时提出一种可整合多种检测原因以提高精度的分层估计量。通过模拟研究验证了所提方法的性能。