At the time medical products are approved, we rarely know enough about their comparative safety and effectiveness vis-à-vis alternative therapies to advise patients and providers. Postmarket evidence generation to study rare adverse events following medical product exposure increasingly requires analysis of millions of longitudinal patient records that can provide complete capture of patient experiences. In the article by Pradhan et al. (Am J Epidemiol. Glucagon-Like Peptide-1 Receptor Agonists and Risk of Anaphylactic Reaction Among Patients With Type 2 Diabetes: Multisite Population-Based Cohort Study), the authors demonstrate how observational database studies are often the most practical approach, provided these databases are carefully chosen to be fit for purpose. Distributed data networks with common data models have proliferated in the last two decades in pharmacoepidemiology, allowing efficient capture of patient data in standardized and structured format across disparate real-world data sources. Use of common data models facilitates transparency by allowing standardized programming approaches that can be easily reproduced. The distributed data network architecture, combined with a common data approach, supports not only multi-site observational studies but also pragmatic clinical trials. It also helps bridge international boundaries and further increases sample size and diversity of study population.
Am J Epidemiol
Go BIG and Go Global: Executing Large-Scale, Multi-Site Pharmacoepidemiologic Studies Using Real-world Data.