A risk-varying and perturbed self-controlled case series design for assessing the safety of COVID-19 vaccines in a large health care system

项目总结/摘要 尽管美国史无前例地成功推出了COVID-19疫苗，疫苗犹豫是显而易见的， 部分原因是对围绕mRNA新技术的严重不良事件（SAE）的安全性担忧 COVID-19疫苗和收到杨森COVID-19疫苗后的血栓报告。而 严格的安全监测可能有助于支持COVID-19疫苗接种，但在方法上具有挑战性， 彻底评估两剂mRNA COVID-19疫苗和一剂Janssen COVID-19疫苗的安全性， 19疫苗。现有的方法可以产生假阳性和假阴性信号时，1）风险窗口 2）在风险窗口期内SAE的恒定风险被错误地指定为 假设，3）可能影响接受第二剂mRNA COVID-19疫苗的因素不包括 解释，以及4）第一个和第二个潜在重叠风险窗口期间SAE风险的性质 不评估第二剂mRNA COVID-19疫苗。 作为对FOA PA-18-873的回应，本提案提出了具体目标：“创建/评估 分析疫苗安全性数据的统计方法，包括现有数据中的数据 例如被动报告系统或医疗保健数据库。”我们建议开发新的统计 模型，通过允许风险水平在不同时期变化， 未知的风险窗口，并使用数据驱动的方法来定义这些风险窗口。我们还将创建一个 衡量SAE风险的新指标，同时考虑风险水平和风险窗长度， 解决两种剂量的风险窗口的潜在重叠，并采用倾向评分模型方法， 考虑可能影响接受第二剂mRNA COVID-19疫苗的因素。我们将 建立这些新方法来评估COVID-19疫苗的安全性，并将其应用于现有数据 来自凯撒永久南加州的成员，一个大的，种族，和社会经济多样化的 人口 通过这项研究，我们将发现值得关注的严重不良事件，更好地向公众和政策制定者通报 COVID-19疫苗的安全性，并生成可能有助于临床医生 为有风险的人提供适当的护理。

PROJECT SUMMARY/ABSTRACT Despite the success of the unprecedented COVID-19 vaccine rollout in the U.S., vaccine hesitancy is evident, partly due to safety concerns about severe adverse events (SAEs) surrounding the novel technology of mRNA COVID-19 vaccines and reports of blood clots following receipt of the Janssen COVID-19 vaccine. While rigorous safety monitoring may help support COVID-19 vaccination, it is methodologically challenging to thoroughly evaluate the safety of the two-dose mRNA COVID-19 vaccines and the one-dose Janssen COVID- 19 vaccine. Existing approaches can produce false positive and false negative signals when 1) risk windows after vaccination are incorrectly specified, 2) a constant risk of SAEs during the risk window is wrongly assumed, 3) factors that may influence receipt of the second dose of mRNA COVID-19 vaccines are not accounted for, and 4) the nature of the risk of SAEs during potential overlapping risk windows of the first and second doses of mRNA COVID-19 vaccines is not assessed. In response to the FOA, PA-18-873, this proposal addresses the specific objective: “creation/evaluation of statistical methodologies for analyzing data on vaccine safety, including data available from existing data sources such as passive reporting systems or healthcare databases.” We propose to develop novel statistical models to properly measure the risk of new COVID-19 vaccines by allowing the risk level to vary during unknown risk windows and using a data-driven approach to define these risk windows. We will also create a new metric for measuring the risk of SAEs considering both the risk level and the length of the risk window, address the potential overlap of risk windows of two doses, and employ a propensity score model approach to account for factors that may influence receipt of the second dose of mRNA COVID-19 vaccines. We will establish these novel approaches to evaluate COVID-19 vaccine safety and will apply them to existing data from members of Kaiser Permanente Southern California, a large, racially, and socio-economically diverse population. Through this research, we will detect SAEs of concern, better inform the public and policymakers about the safety of COVID-19 vaccines, and generate vaccine safety information that may be helpful for clinicians to deliver appropriate care to those at risk.