Common Models, Different Approaches

In recent years, a number of initiatives have established database networks for studying drug safety, including the Mini-Sentinel [1] and Observational Medical Outcomes Partnership (OMOP) [2] programs in the US, the Canadian Network for Observational Drug Effect Studies (CNODES) [3], the Asian Pharmacoepidemiology Network (AsPEN) [4], and the Exploring and Understanding Adverse Drug Reactions (EU-ADR) in Europe [5]. These networks, each comprising data for up to hundreds of millions of individuals, facilitate analyses on unprecedented numbers of patients, which can be particularly useful for evaluating very rare adverse outcomes, investigating heterogeneity across patient subgroups, or assessing outcomes shortly after drug launch, when the number of exposed individuals in any one database may be limited. Some, but not all, initiatives have adopted common data models (CDMs) to standardize the data structure across the often diverse databases. In particular, the US-based programs, Mini-Sentinel and OMOP, developed separate CDMs and have created tools compatible with the respective CDMs to quickly perform standardized analyses across the database networks [6, 7]. The US Food and Drug Administration (FDA) is now using results from analyses conducted in the Mini-Sentinel CDM to inform regulatory decision making, and data transformed into the OMOP CDM are available from the Reagan-Udall Foundation for the FDA’s Innovation in Medical Evidence Development and Surveillance program, which aims to facilitate methods research for medical product safety monitoring, among other objectives. Given the potential regulatory and public health importance of results arising from these programs, it is critical to understand the impact of a CDM on the ability to conduct robust medical product safety surveillance. The ambitious study by Xu and colleagues in this issue of Drug Safety is an important step in this direction [8]. Using Humana’s claims database, which they transformed into both the Mini-Sentinel and the OMOP CDMs, the authors conducted what they call an ‘ecosystem’ comparison by evaluating the results of analyses in the two CDMs using tools that were developed for use in these environments, holding the underlying data constant. Using six drug–outcome pairs for which positive associations are expected, the authors compared what they call the ‘highdimensional propensity score(hdPS-) based analysis procedures’ developed for each CDM and the ‘self-controlled case series (SCCS) analysis procedure’ for each CDM. The authors also compared the CDMs on a conceptual level, elucidating a number of important differences between the two. A particularly salient difference is evident in the sometimes large differences in numbers of patients each approach identifies as being exposed to particular medical products. For example, the application of the ‘hdPS-based analysis procedure’ in the OMOP environment identified 356,078 new users of ketorolac, whereas the application in the Mini-Sentinel environment identified 30,322 new users (\9 % of OMOP total). As the authors explain, they used only national drug codes (NDCs) to identify ketorolac exposure in the Mini-Sentinel analysis, but the concept-based identification process used in the OMOP CDM also captured drug exposure using procedure & Joshua J. Gagne jgagne1@partners.org