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

项目摘要/摘要 尽管史无前例的新冠肺炎疫苗在美国的推出取得了成功，但疫苗的犹豫不决是显而易见的。 部分原因是对围绕mRNA新技术的严重不良事件(SAE)的安全担忧 新冠肺炎疫苗和收到扬森新冠肺炎疫苗后出现血栓的报告。而当 严格的安全监测可能有助于支持新冠肺炎疫苗接种，在方法上是有挑战性的 彻底评价两剂新冠肺炎疫苗和一剂扬森冠状病毒疫苗的安全性 19种疫苗。现有方法会在以下情况下产生假阳性和假阴性信号：1)风险窗口 在错误地指定疫苗接种之后，2)在风险窗口期间持续的SAE风险是错误的 假设，3)可能影响第二针新冠肺炎疫苗接种的因素不是 以及4)在第一个和第二个潜在重叠风险窗口期间SAE的风险性质 没有对第二剂新冠肺炎疫苗进行评估。 针对PA-18-873的FOA，本提案涉及具体目标：“创建/评价 分析疫苗安全性数据的统计方法，包括现有数据 被动报告系统或医疗保健数据库等来源。我们建议开发新的统计方法 通过允许风险水平在接种期间变化来适当衡量新新冠肺炎疫苗风险的模型 未知风险窗口，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，同时考虑了风险水平和风险窗口的长度， 解决两种剂量的风险窗口可能重叠的问题，并采用倾向评分模型方法 解释可能影响第二剂新冠肺炎基因疫苗接种的因素。我们会 建立这些新方法来评估新冠肺炎疫苗的安全性，并将它们应用于现有数据 来自南加州Kaiser Permanente的成员，一个庞大的，种族和社会经济多元化的 人口。 通过这项研究，我们将发现值得关注的SAE，更好地向公众和政策制定者通报 新冠肺炎疫苗的安全性，并生成可能有助于临床医生 为那些处于危险中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.