Am J Epidemiol. 2021 Sep 1;190(9):1882-1889. doi: 10.1093/aje/kwab066.
The test-negative study design is often used to estimate vaccine effectiveness in influenza studies, but it has also been proposed in the context of other infectious diseases, such as cholera, dengue, or Ebola. It was introduced as a variation of the case-control design, in an attempt to reduce confounding bias due to health-care-seeking behavior, and has quickly gained popularity because of its logistic advantages. However, examination of the directed acyclic graphs that describe the test-negative design reveals that without strong assumptions, the estimated odds ratio derived under this sampling mechanism is not collapsible over the selection variable, such that the results obtained for the sampled individuals cannot be generalized to the whole population. In this paper, we show that adjustment for severity of disease can reduce this bias and, under certain assumptions, makes it possible to unbiasedly estimate a causal odds ratio. We support our findings with extensive simulations and discuss them in the context of recently published cholera test-negative studies of the effectiveness of cholera vaccines.
病例对照研究设计常用于流感研究中评估疫苗效果，但也有研究将其应用于其他传染病，如霍乱、登革热或埃博拉。该设计作为病例对照设计的一种变体，旨在减少因寻求医疗保健而导致的混杂偏倚，由于其具有逻辑优势，因此迅速流行起来。然而，对描述病例对照研究的有向无环图的检验表明，如果没有严格的假设，在这种抽样机制下得出的估计比值在选择变量上是不可分解的，因此从抽样个体中获得的结果不能推广到整个人群。在本文中，我们表明，调整疾病严重程度可以减少这种偏差，并且在某些假设下，可以无偏地估计因果比值。我们通过广泛的模拟来支持我们的发现，并在最近发表的霍乱疫苗对霍乱有效性的病例对照研究的背景下讨论这些发现。
