A Double Machine Learning Approach for the Evaluation of COVID-19 Vaccine Effectiveness under the Test-Negative Design: Analysis of Qu\'ebec Administrative Data

TL;DR

This paper introduces a novel double machine learning estimator, TNDDR, for evaluating COVID-19 vaccine effectiveness using test-negative design data, enhancing confounder adjustment with theoretical and empirical support.

Contribution

It proposes a new doubly robust, locally efficient estimator that incorporates machine learning for better confounder control in COVID-19 VE assessment under TND.

Findings

The TNDDR estimator achieves $\

The estimator is both theoretically justified and empirically validated.

It improves confounder adjustment in vaccine effectiveness studies.

Abstract

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 Qu\'ebec, 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 $\geq 60$ years in Qu\'ebec, 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 $\sqrt{n}-$consistency, asymptotic normality and double robustness of TNDDR. The proposed estimator is supported by both theoretical and empirical justifications.