The test-negative design (TND) is a popular method for evaluating vaccine effectiveness (VE). A "classical" TND study includes symptomatic individuals tested for the disease targeted by the vaccine to estimate VE against symptomatic infection. However, recent applications of the TND have attempted to estimate VE against infection by including all tested individuals, regardless of their symptoms. In this article, we employ directed acyclic graphs and simulations to investigate potential biases in TND studies of COVID-19 VE arising from the use of this "alternative" approach, particularly when applied during periods of widespread testing. We show that the inclusion of asymptomatic individuals can potentially lead to collider stratification bias, uncontrolled confounding by health and healthcare-seeking behaviors (HSBs), and differential outcome misclassification. While our focus is on the COVID-19 setting, the issues discussed may also be relevant in the context of other infectious diseases. This may be particularly true in scenarios where there is either a high baseline prevalence of infection, a strong correlation between HSBs and vaccination, different testing practices for vaccinated and unvaccinated individuals, or settings where both the vaccine under study attenuates symptoms of infection and diagnostic accuracy is modified by the presence of symptoms.
Competing Interest Statement .
The authors have declared no competing interest.
Funding Statement .
Work supported by the Canadian Institutes of Health Research Project Grant ECP-184178 (awarded to MES, DT, JM, CJ). MES holds the Canada Research Chair in Causal Inference and Machine Learning in Health Science. MC holds a Chercheur Boursier Junior 1 Award from the Fonds de recherche du Quebec-Sante (FRQS). DT holds a Chercheur Boursier Junior 2 Award from FRQS. E-OB holds a Doctoral Training Award from FRQS.
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Data Availability .
Data and code are available in a repository cited in the manuscript.