In this paper, we develop a Bayesian calendar-time survival model motivated by infectious disease prevention studies occurring during an epidemic, when the risk of infection can change rapidly as the epidemic curve shifts. For studies in which a biomarker is the predictor of interest, we include the option to estimate a threshold of protection for the biomarker. If the intervention is hypothesized to have different associations with several circulating viral variants, or if the infectiousness of the dominant variant(s) changes over the course of the study, we treat infection from different variants as competing risks. We also introduce a novel method for incorporating existing epidemic curve estimates into an informative prior for the baseline hazard function, enabling estimation of the intervention's association with infection risk during periods of calendar time with minimal follow-up in one or more comparator groups. We demonstrate the strengths of this method via simulations, and we apply it to data from an observational COVID-19 vaccine study.

翻译：本文提出了一种贝叶斯日历时间生存模型，其研究背景为流行病期间的传染病预防研究——当疫情曲线波动时，感染风险可能发生快速变化。对于以生物标志物为核心预测指标的研究，本模型提供了估计生物标志物保护阈值的可选功能。若假设干预措施与多种流行病毒变异株存在差异关联，或主要变异株的传染性在研究期间发生变化，本模型将不同变异株的感染事件视为竞争风险。同时，我们提出了一种创新方法，可将现有流行病曲线估计值整合至基线风险函数的先验信息中，从而实现在一个或多个对照组随访数据极少的日历时间段内，仍能有效估计干预措施与感染风险的关联性。通过模拟实验验证了该方法的优势，并将其应用于一项COVID-19疫苗观察性研究的数据分析。