Safety Surveillance Research, Worldwide Medical and Safety, Pfizer Inc, New York, NY.
Department of Family and Community Medicine, New York Medical College, Valhalla, NY and Truliant Consulting, Baltimore, Maryland.
Clin Ther. 2024 Jul;46(7):555-564. doi: 10.1016/j.clinthera.2024.06.005. Epub 2024 Aug 13.
Well-designed observational postmarketing studies using real-world data (RWD) are critical in supporting an evidence base and bolstering public confidence in vaccine safety. This systematic review presents current research methodologies in vaccine safety research in postapproval settings, technological advancements contributing to research resources and capabilities, and their major strengths and limitations.
A comprehensive search was conducted using PubMed to identify relevant articles published from January 1, 2019, to December 31, 2022. Eligible studies were summarized overall by study design and other study characteristics (eg, country, vaccine studied, types of data source, and study population). An in-depth review of select studies representative of conventional or new designs, analytical approaches, or data collection methods was conducted to summarize current methods in vaccine safety research.
Out of 977 articles screened for inclusion, 135 were reviewed. The review shows that recent advancements in scientific methods, digital technology, and analytic approaches have significantly contributed to postapproval vaccine safety studies using RWD. "Near real-time surveillance" using large datasets (via collaborative or distributed databases) has been used to facilitate rapid signal detection that complements passive surveillance. There was increasing appreciation for self-controlled case-only designs (self-controlled case series and self-controlled risk interval) to assess acute-onset safety outcomes, artificial intelligence, and natural language processing to improve outcome accuracy and study timeliness and emerging artificial intelligence-based analysis to capture adverse events from social media platforms.
Continued development in the area of vaccine safety research methodologies using RWD is warranted. The future of successful vaccine safety research, especially evaluation of rare safety events, is likely to comprise digital technologies including linking RWD networks, machine learning, and advanced analytic methods to generate rapid and robust real-world safety information.
利用真实世界数据（RWD）进行精心设计的观察性上市后研究对于支持证据基础和增强公众对疫苗安全性的信心至关重要。本系统评价介绍了批准后疫苗安全性研究中当前的研究方法学、推动研究资源和能力的技术进步，以及它们的主要优势和局限性。
使用 PubMed 进行全面检索，以确定 2019 年 1 月 1 日至 2022 年 12 月 31 日期间发表的相关文章。根据研究设计和其他研究特征（例如，国家、研究疫苗、数据源类型和研究人群）对合格研究进行总体总结。对具有代表性的传统或新型设计、分析方法或数据收集方法的选定研究进行深入审查，以总结疫苗安全性研究中的当前方法。
在筛选的 977 篇文章中，有 135 篇被审查。综述表明，科学方法、数字技术和分析方法的最新进展极大地促进了使用 RWD 的批准后疫苗安全性研究。利用大型数据集（通过协作或分布式数据库）进行的“近实时监测”已被用于促进快速信号检测，以补充被动监测。越来越重视自我对照病例对照设计（自我对照病例系列和自我对照风险间隔）来评估急性发作的安全性结局、人工智能和自然语言处理以提高结果准确性和研究及时性，以及新兴的基于人工智能的分析以从社交媒体平台捕获不良事件。
有必要继续开发使用 RWD 的疫苗安全性研究方法学领域。成功的疫苗安全性研究的未来，特别是对罕见安全性事件的评估，可能包括数字技术，包括链接 RWD 网络、机器学习和先进的分析方法，以生成快速而稳健的真实世界安全性信息。
