Improving Assessment of Vaccine Effectiveness by Coupling Test-negative Design Studies with Survival Models

The test-negative design (TND) has become a widely used observational study design for evaluating vaccine effectiveness, especially during the COVID-19 pandemic. Traditionally, TND has been viewed as a variant of the case-control study and largely limited to use with logistic regression models. In this paper, we first establish that TND can be framed as a special case of a cohort study, thereby opening the door to a wider range of analytical approaches. We then introduce the Prentice, Williams, and Peterson gap-time (PWP-GT) frailty model as a novel method for analyzing TND data, accounting for recurrent infections and time-dependent vaccination status. Through extensive simulation studies, we demonstrate that the proposed model outperforms conventional models commonly applied in TND-based vaccine effectiveness studies. Finally, we apply our method to data from the National COVID Cohort Collaborative, estimating the effectiveness of full and booster doses of Pfizer's COVID-19 vaccines against both initial infection and reinfection during the Omicron variant circulation period in a real-world setting.