Bate Andrew, Chuang-Stein Christy, Roddam Andrew, Jones Byron
Pfizer, Tadworth, UK.
New York University, New York, NY, USA.
Pharm Stat. 2019 Jan;18(1):65-77. doi: 10.1002/pst.1908. Epub 2018 Oct 25.
Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real-world use. Such databases are frequently configured as distributed networks. That is, patient-level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU-ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well-established field of meta-analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta-analysis to help guide the development of distributed network analyses of longitudinal observational databases.
由纵向观测数据库（通常是电子病历或交易性保险理赔数据或两者兼有）构成的星座式网络越来越多地被用于研究药品在实际使用中的效果。此类数据库通常被配置为分布式网络。也就是说，患者层面的数据存放在防火墙之后，除了汇总形式外，不会在数据供应商之外进行传输。相反，数据在整个网络中进行标准化处理，网络查询由数据合作伙伴在本地执行，汇总结果提供给中央研究合作伙伴进行合并、聚合和总结。此类网络规模可能非常庞大，涵盖数亿患者多年的数据。此类网络的例子包括美国食品药品监督管理局哨兵网络（FDA Sentinel Network）、药物流行病学系统评价协作组（ASPEN）、国家药品不良反应监测中心网络（CNODES）和欧盟药品不良反应数据库（EU-ADR）。由于这是一个新兴领域，我们在本文中指出分布式网络分析与现已成熟的随机临床试验（RCT）荟萃分析领域之间在概念上的异同。我们建议在适当的情况下应用荟萃分析的经验教训，以帮助指导纵向观测数据库分布式网络分析的发展。
