Although postmarketing studies conducted in population‐based databases often contain information on patients in the order of millions, they can still be underpowered if outcomes or exposure of interest is rare, or the interest is in subgroup effects. Combining several databases might provide the statistical power needed. A multi‐database study (MDS) uses at least two healthcare databases, which are not linked with each other at an individual person level, with analyses carried out in parallel across each database applying a common study protocol. Although many MDSs have been performed in Europe in the past 10 years, there is a lack of clarity on the peculiarities and implications of the existing strategies to conduct them. In this review, we identify four strategies to execute MDSs, classified according to specific choices in the execution: (A) local analyses , where data are extracted and analyzed locally, with programs developed by each site; (B) sharing of raw data , where raw data are locally extracted and transferred without analysis to a central partner, where all the data are pooled and analyzed; (C) use of a common data model with study‐specific data , where study‐specific data are locally extracted, loaded into a common data model, and processed locally with centrally developed programs; and (D) use of general common data model , where all local data are extracted and loaded into a common data model, prior to and independent of any study protocol, and protocols are incorporated in centrally developed programs that run locally. We illustrate differences between strategies and analyze potential implications.

中文翻译：

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在现实世界环境中执行药物监测多数据库研究的不同策略：对欧洲模式的反思。


尽管在基于人群的数据库中进行的上市后研究通常包含数百万患者的信息，但如果感兴趣的结果或暴露很少，或者感兴趣的是亚组效应，那么它们仍然可能不够有力。组合多个数据库可能会提供所需的统计能力。多数据库研究 (MDS) 至少使用两个医疗保健数据库，这些数据库在个人层面上彼此不相关，并采用通用研究方案在每个数据库中并行进行分析。尽管过去 10 年在欧洲开展了许多 MDS，但目前开展这些活动的现有战略的特殊性和影响尚不明确。在本次审查中，我们确定了执行 MDS 的四种策略，并根据执行中的具体选择进行分类： （A）本地分析，在本地提取和分析数据，并使用每个站点开发的程序； (B) 共享原始数据，原始数据在本地提取并传输至中央合作伙伴，所有数据均在中央合作伙伴处进行汇总和分析； (C) 使用具有研究特定数据的通用数据模型，其中研究特定数据在本地提取、加载到通用数据模型中，并使用集中开发的程序在本地进行处理； (D) 使用通用通用数据模型，其中所有本地数据都在任何研究协议之前并独立于任何研究协议被提取并加载到通用数据模型中，并且协议被合并到在本地运行的集中开发的程序中。我们说明策略之间的差异并分析潜在影响。