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Different Strategies to Execute Multi‐Database Studies for Medicines Surveillance in Real‐World Setting: A Reflection on the European Model
Author(s) -
Gini Rona,
Sturkenboom Miriam C. J.,
Sultana Janet,
Cave Alison,
Landi Annalisa,
Pacurariu Alexandra,
Roberto Giuseppe,
Schink Tania,
Candore Gianmario,
Slattery Jim,
Trifirò Gianluca
Publication year - 2020
Publication title -
clinical pharmacology and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.941
H-Index - 188
eISSN - 1532-6535
pISSN - 0009-9236
DOI - 10.1002/cpt.1833
Subject(s) - computer science , raw data , database , protocol (science) , data mining , population , data sharing , medicine , programming language , alternative medicine , environmental health , pathology
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.