An analytic approach considering two temporal mechanisms driving breakthrough viral infections after vaccination.

Real world data is an increasingly utilized resource for post-market monitoring of vaccines and provides insight into real world effectiveness. However, heterogeneous mechanisms may drive observed breakthrough infections among vaccinated individuals, such as waning vaccine-induced immunity or the emergence of a new strain against which the vaccine has reduced protection. Analyses of breakthrough infection incidence rates are typically predicated on a presumed temporal mechanism in their choice of an "analytic time zero" after which infection rates are modeled. In this work, we propose a test that utilizes a standard Cox proportional hazards framework to investigate two temporal mechanisms that can drive breakthrough infections of viral pathogens: waning immunity and the emergence of new strain. We explore the test's performance in simulation studies and in an illustrative application to real world data. We additionally introduce subgroup differences in infection incidence and evaluate the impact of time zero misspecification on bias and coverage of model estimates. In this study we observe strong power and controlled type I error of the test to detect true waning immunity effects under various settings. Similar to previous studies, we find mitigated bias and greater coverage of estimates when the analytic time zero is correctly specified or accounted for.