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Comparison of the Test-negative Design and Cohort Design With Explicit Target Trial Emulation for Evaluating COVID-19
Guilin Li1,2, Hanna Gerlovin3, Michael J Figueroa Muñiz3,4
1From the CAUSALab, Harvard T.H. Chan School of Public Health, Boston, MA.
Background:
Observational studies are used for estimating vaccine effectiveness under real-world conditions. The practical performance of two common approaches-cohort and test-negative designs-need to be compared for COVID-19 vaccines.
Methods:
We compared the cohort and test-negative designs to estimate the effectiveness of the BNT162b2 vaccine against COVID-19 outcomes using nationwide data from the United States Department of Veterans Affairs. Specifically, we (1) explicitly emulated a target trial using follow-up data and evaluated the potential for confounding using negative controls and benchmarking to a randomized trial, (2) performed case-control sampling of the cohort to confirm empirically that the same estimate is obtained, (3) further restricted the sampling to person-days with a test, and (4) implemented additional features of a test-negative design. We also compared their performance in limited datasets.
Results:
Estimated BNT162b2 vaccine effectiveness was similar under all four designs. Empirical results suggested limited residual confounding by healthcare-seeking behavior. Analyses in limited datasets showed evidence of residual confounding, with estimates biased downward in the cohort design and upward in the test-negative design.
Conclusion:
Vaccine effectiveness estimates under a cohort design with explicit target trial emulation and a test-negative design were similar when using rich information from the VA healthcare system, but diverged in opposite directions when using a limited dataset. In settings like ours with sufficient information on confounders and other key variables, the cohort design with explicit target trial emulation may be preferable as a principled approach that allows estimation of absolute risks and facilitates interpretation of effect estimates.
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