The widespread testing for severe acute respiratory syndrome coronavirus 2 infection has facilitated the use of test-negative designs (TNDs) for modeling coronavirus disease 2019 (COVID-19) vaccination and outcomes. Despite the comprehensive literature on TND, the use of TND in COVID-19 studies is relatively new and calls for robust design and analysis to adapt to a rapidly changing and dynamically evolving pandemic and to account for changes in testing and reporting practices. In this commentary, we aim to draw the attention of researchers to COVID-specific challenges in using TND as we are analyzing data amassed over more than two years of the pandemic. We first review when and why TND works and general challenges in TND studies presented in the literature. We then discuss COVID-specific challenges which have not received adequate acknowledgment but may add to the risk of invalid conclusions in TND studies of COVID-19.
Keywords: COVID-19; control selection; observational data; symptomatic testing; test-negative design; time-varying confounding; vaccine effectiveness.
© The Author(s) 2022. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.