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Multi-database distributed data networks for post-marketing surveillance of drug safety and effectiveness use two main approaches: common data models (CDMs) and common protocols. Networks such as the U.S. Sentinel System, the Observational Health Data Sciences and Informatics (OHDSI) network, and the Data Analysis and Real-World Interrogation Network in Europe (DARWIN-EU) use a CDM approach in which participating databases are translated into a standardized structure so that a single, common analytic program can be used. On the other hand, the common protocol approach involves applying a single common protocol to site-specific data maintained in their native format, with analytic programs tailored to each data source. Some networks, such as the Canadian Network for Observational Drug Effect Studies (CNODES) and the Asian Pharmacoepidemiology Network (AsPEN), use a variety of approaches for multi-database studies.
In this Core Concepts paper, we review the purpose and different types of distributed data networks in pharmacoepidemiology, discuss their advantages and disadvantages, and describe commonly faced challenges and opportunities in conducting research using multi-database networks.