A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance

Introduction An often key component to coordinating surveillance activities across distributed networks is the design and implementation of a common data model (CDM). The purpose of this study was to evaluate two drug safety surveillance CDMs from an ecosystem perspective to better understand how differences in CDMs and analytic tools affect usability and interpretation of results. Methods Humana claims data from 2007 to 2012 were mapped to Observational Medical Outcomes Partnership (OMOP) and Mini-Sentinel CDMs. Data were described and compared at the patient level by source code and mapped concepts. Study cohort construction and effect estimates were also compared using two different analytical methods—one based on a new user design implementing a high-dimensional propensity score (HDPS) algorithm and the other based on univariate self-controlled case series (SCCS) design—across six established positive drug-outcome pairs to learn how differences in CDMs and analytics influence steps in the database analytic process and results. Results Claims data for approximately 7.7 million Humana health plan members were transformed into the two CDMs. Three health outcome cohorts and two drug cohorts showed differences in cohort size and constituency between Mini-Sentinel and OMOP CDMs, which was a result of multiple factors. Overall, the implementation of the HDPS procedure on Mini-Sentinel CDM detected more known positive associations than that on OMOP CDM. The SCCS method results were comparable on both CDMs. Differences in the implementation of the HDPS procedure between the two CDMs were identified; analytic model and risk period specification had a significant impact on the performance of the HDPS procedure on OMOP CDM. Conclusions Differences were observed between OMOP and Mini-Sentinel CDMs. The analysis of both CDMs at the data model level indicated that such conceptual differences had only a slight but not significant impact on identifying known safety associations. Our results show that differences at the ecosystem level of analyses across the CDMs can lead to strikingly different risk estimations, but this can be primarily attributed to the choices of analytic approach and their implementation in the community-developed analytic tools. The opportunities of using CDMs are clear, but our study shows the need for judicious comparison of analyses across the CDMs. Our work emphasizes the need for ongoing efforts to ensure sustainable transparent platforms to maintain and develop CDMs and associated tools for effective safety surveillance.