Lessons from meta‐analyses of randomized clinical trials for analysis of distributed networks of observational databases

Abstract

Networks of constellations of longitudinal observational databases, often electronic medical records or transactional insurance claims or both, are increasingly being used for studying the effects of medicinal products in real‐world use. Such databases are frequently configured as distributed networks. That is, patient‐level data are kept behind firewalls and not communicated outside of the data vendor other than in aggregate form. Instead, data are standardized across the network, and queries of the network are executed locally by data partners, and summary results provided to a central research partner(s) for amalgamation, aggregation, and summarization. Such networks can be huge covering years of data on upwards of 100 million patients. Examples of such networks include the FDA Sentinel Network, ASPEN, CNODES, and EU‐ADR. As this is a new emerging field, we note in this paper the conceptual similarities and differences between the analysis of distributed networks and the now well‐established field of meta‐analysis of randomized clinical trials (RCTs). We recommend, wherever appropriate, to apply learnings from meta‐analysis to help guide the development of distributed network analyses of longitudinal observational databases.

Analytic and Data Sharing Options in Real‐World Multidatabase Studies of Comparative Effectiveness and Safety of Medical Products

“…In principle, researchers should present the overall and database-specific effect estimates (e.g., using forest plots) in REVIEW multidatabase studies. 55,72 However, certain data-contributing sites may be concerned about this practice even if the site identities are masked. For example, one of the largest (or smallest) organizations may have a higher mortality risk compared with other sites simply because it serves an older or sicker population.…”

“…In principle, researchers should present the overall and database‐specific effect estimates (e.g., using forest plots) in multidatabase studies . However, certain data‐contributing sites may be concerned about this practice even if the site identities are masked.…”

The Nordic perioperative and intensive care registries—Collaboration and research possibilities

“…The newly published study on Nordic COVID‐19 ICU patients is an example of this method 22 . The EU‐ADR project on adverse drug reactions has used this methodology extensively 45,46 . A Nordic study on pregnancy outcomes compared individual‐based and such aggregate meta‐analysis 47 …”

“…The used this methodology extensively. 45,46 A Nordic study on pregnancy outcomes compared individual-based and such aggregate metaanalysis. 47…”

Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions

“… 17 Since the early 1990s, the number of publications internationally referring to multiple observational databases has exploded thanks to increased existence of electronic databases, the appreciation of the importance of data-linkage and information technology for analysing multiple databases. 18 Recent collaborations such as Vaccine Adverse Events Monitoring and Communication (VAESCO), Accelerated Development of Vaccine Benefit-risk Collaboration in Europe (ADVANCE/VAC4EU), Systematic Observational Method for Narcolepsy and Influenza Immunisation Assessment (SOMNIA) and the WHO Global Vaccine Safety-Multi Country Collaboration (GVS-MCC) have successfully incorporated data from multiple countries to produce estimates of vaccine safety and effectiveness. 12 19–22 The purpose of this paper is to appraise current methodologies in postmarketing vaccine safety in terms of how these can be used to assess rare events and special populations, and how evidence derived from these studies can be evaluated.…”