Different Strategies to Execute Multi-Database Studies for Medicines Surveillance in Real-World Setting: A Reflection on the European Model

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

Research and Practical Experiences in the Use of

this document provided that it is printed in its entirety 2 Executive Summary increasingly important. Information sharing among organizations can help achieve important public benefits such as increased productivity, improved policy-making, and integrated public services. This paper reviews uses of multiple data sources for enterprise-level planning and decision making. It identifies current research and practical experience in the use of multiple data sources to support performance measurement, strategic planning, and interorganizational business processes. The information was derived from journal articles and Internet sources. A series of cases are examined, and the benefits, issues, methods, and results of efforts that involve the integration of different data sources in the same organization and across multiple organizations are identified and compared. The purpose of this paper is to take the first steps towards the development of a methodology for integrating multiple data source

Zero-Latency Data Warehousing For

In this paper, a framework for building an overall Zero-Latency Data Warehouse system (ZLDWH) is provided. Such a ZLDWH requires tasks such as data changes detection, continuous loading and updating of new data, autonomous analysis and the execution of actions to be completed in the Data Warehouse (DWH). For this purpose, we combine (1) an existing solution for the continuous data integration and (2) the known approach of Active Data Warehousing (ADWH) by introducing protocols that enable the correct collaboration between the two. The continuous data integration is realized on the one hand by asynchronous communication between multiple information sources and the DWH through a message queuing system, and on the other hand by the continuous loading and updating. To obtain a low-latency time for this cycle, an "active rule evaluation" is provided between the data feeding stage and its loading into the DWH. This is realized by applying ECA-Techniques (EventCondition -Action) to obtain a more "active" characteristic of the DWH