Estimands and estimation of COVID-19 vaccine effectiveness under the test-negative design: connections to causal inference (Mireille Schnitzer -- University of Montreal)

The test-negative design (TND) is routinely used for the monitoring of seasonal flu vaccine effectiveness. More recently, it has become integral to the estimation of COVID-19 vaccine effectiveness, in particular for more severe disease outcomes. Distinct from the case-control study, the design typically involves recruitment of participants with a common symptom presentation who are being tested for the infectious disease in question. Participants who test positive for the target infection are the “cases” and those who test negative are the “controls”. Logistic regression is the only statistical method that has been proposed to estimate vaccine effectiveness under the TND while adjusting for confounders. While under strong modeling assumptions it produces estimates of a causal risk ratio, it may be biased in the presence of effect modification by a confounder. I will present and justify an inverse probability of treatment weighting (IPTW) estimator for the marginal risk ratio, which is valid under effect modification. I’ll discuss connections between the estimands targeted by these two methods and causal parameters under different interference assumptions. I will then describe the results of a simulation study to illustrate and confirm the derivations and to evaluate the performance of the estimators.