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

Abstract

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.

Safety assessment of niacin in the US Food and Drug Administration's mini‐sentinel system

“…The Mini‐Sentinel pilot (now the Sentinel System) uses a distributed database that allows data to be stored locally under the control of participating Data Partners, which contribute, and regularly update, administrative claims and clinical information in a common data model . As of August 2015 (around the time of the analysis), the distributed database comprised data on approximately 193 million covered lives from 18 Data Partners, including 39 million individuals who were actively enrolled and contributing data.…”

Choosing Among Common Data Models for Real‐World Data Analyses Fit for Making Decisions About the Effectiveness of Medical Products

“…This adaptability of defining exposure, outcome, or patient characteristics, for example, is best informed by the investigator who is implementing a specific study and by the people closest to the data‐generation process, who have insight into true recording validity and completeness; though for practical reasons, developers of universal mapping algorithms used in mapping CDMs work with a generic use case in mind, which may not be easily adaptable to a specific study setting. Problems have been reported, some related to mistaken assumptions about drug exposure and others about specific outcomes, reporting very different measurement characteristics in terms of sensitivity and specificity . In longitudinal data, calendar time and event time are important aspects defining critical study time periods, which can bias results .…”

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Datasets

“…In some cases, adherence to a CDM is a prerequisite for participating on a grant (or research network). Wider adoption of CDMs8,9 also facilitates development of data quality tools that can be easily applied across multiple data sets.…”