Real world data is an increasingly utilized resource for post-market monitoring of vaccines and provides insight into real world effectiveness. However, outside of the setting of a clinical trial, heterogeneous mechanisms may drive observed breakthrough infection rates among vaccinated individuals; for instance, waning vaccine-induced immunity as time passes and the emergence of a new strain against which the vaccine has reduced protection. Analyses of infection incidence rates are typically predicated on a presumed mechanism in their choice of an "analytic time zero" after which infection rates are modeled. In this work, we propose an explicit test for driving mechanism situated in a standard Cox proportional hazards framework. 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 the correct infection-driving mechanism 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.

翻译：真实世界数据正日益成为疫苗上市后监测的重要资源，可为真实世界有效性提供洞见。然而，在临床试验环境之外，异质性机制可能驱动已接种个体中观察到的突破性感染率变化，例如：随时间推移出现的疫苗诱导免疫力衰减，以及疫苗保护效力降低的新毒株出现。感染发病率的分析通常基于预设机制选择"分析时间零点"（即建模感染发生率后的起始时间）。本研究提出一种基于标准Cox比例风险框架的驱动机制显式检验方法。我们通过模拟研究探索该检验的性能，并在真实世界数据中进行了示例性应用。此外，本研究还引入感染发病率的亚组差异，评估时间零点设定错误对模型估计偏倚和覆盖概率的影响。研究表明，该检验在不同场景下均能有效检测正确感染驱动机制（统计功效强且I类错误可控）。与既往研究一致，我们发现当分析时间零点被正确设定或校正时，估计偏倚得到缓解且覆盖概率更高。