LAUSR

The test-negative design is often used to estimate vaccine effectiveness in influenza studies, but 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 healthcare-seeking behaviour, and has quickly gained popularity due to its logistic advantages. However, examining the directed acyclic graphs that describe the test-negative design reveals that, without strong assumptions, the estimated odds ratio under this sampling mechanism is not collapsible over the selection variable, such that the results obtained for the sampled individuals cannot be generalised to the whole population. In this paper, we show that adjusting 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 vaccine effectiveness studies.