Key result
Novel machine learning TNDDR estimator improves confounder adjustment when estimating COVID-19 vaccine effectiveness.
Why the study?
Efficiency bounds for nonparametric estimators of target parameters under the test-negative design had not yet been investigated to improve adjustment for measured confounders when estimating COVID-19 vaccine effectiveness.
The study proposes a novel doubly robust machine learning estimator (TNDDR) to improve confounding adjustment when evaluating vaccine effectiveness using the test-negative design.
Answers are grounded in this paper and cite their sources.
May refine confounder adjustment in test-negative vaccine studies; leaves open real-world impact pending validation.
The test‐negative design (TND), which is routinely used for monitoring seasonal flu vaccine effectiveness (VE), has recently become integral to COVID‐19 vaccine surveillance, notably in Québec, Canada. Some studies have addressed the identifiability and estimation of causal parameters under the TND, but efficiency bounds for nonparametric estimators of the target parameter under the unconfoundedness assumption have not yet been investigated. Motivated by the goal of improving adjustment for measured confounders when estimating COVID‐19 VE among community‐dwelling people aged years in Québec, we propose a one‐step doubly robust and locally efficient estimator called TNDDR (TND doubly robust), which utilizes cross‐fitting (sample splitting) and can incorporate machine learning techniques to estimate the nuisance functions and thus improve control for measured confounders. We derive the efficient influence function (EIF) for the marginal expectation of the outcome under a vaccination intervention, explore the von Mises expansion, and establish the conditions for ‐consistency, asymptotic normality, and double robustness of TNDDR. The proposed estimator is supported by both theoretical and empirical justifications.
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Jiang et al. (2025) studied COVID-19. TNDDR (TND doubly robust) estimator was evaluated on COVID-19 vaccine effectiveness (methodological evaluation). A proposed one-step doubly robust and locally efficient estimator (TNDDR) utilizing machine learning improves adjustment for measured confounders when estimating COVID-19 vaccine effectiveness.
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