An Analytic Approach for Understanding Mechanisms Driving Breakthrough Infections

Real world data is an increasingly utilized resource for post-marketmonitoring of vaccines and provides insight into real world effectiveness.However, outside of the setting of a clinical trial, heterogeneous mechanismsmay drive observed breakthrough infection rates among vaccinated individuals;for instance, waning vaccine-induced immunity as time passes and the emergenceof a new strain against which the vaccine has reduced protection. Analyses ofinfection incidence rates are typically predicated on a presumed mechanism intheir choice of an "analytic time zero" after which infection rates aremodeled. In this work, we propose an explicit test for driving mechanismsituated in a standard Cox proportional hazards framework. We explore thetest's performance in simulation studies and in an illustrative application toreal world data. We additionally introduce subgroup differences in infectionincidence and evaluate the impact of time zero misspecification on bias andcoverage of model estimates. In this study we observe strong power andcontrolled type I error of the test to detect the correct infection-drivingmechanism under various settings. Similar to previous studies, we findmitigated bias and greater coverage of estimates when the analytic time zero iscorrectly specified or accounted for.