Demonstrating the Application of Real-World Evidence for Health Technology Assessment Using a Federated Data Network

ABSTRACT

Objectives: The European Health Data and Evidence Network (EHDEN.eu) is a large-scale federated network in development of real-world data from across Europe, standardised to the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM). We demonstrate how EHDEN can facilitate the generation of real-world evidence (RWE) to support health technology assessment (HTA) in the context of cancer and COVID-19 use cases. Method(s) The cancer use case employs observational data from EHDEN partners to examine the real-world survival associated with common cancers (e.g., breast, lung) and one rarer cancer (head and neck). Using data from the UK Clinical Practice Research Datalink in the first instance, a user-friendly dashboard provides HTA agencies and researchers with a quick tool to view natural history data for the selected cancers and examine long-term survival projections using different parametric survival functions. The COVID-19 use case includes a multi-database network comparative cohort study, using observational EHDEN data, and explores how these data can be meta-analysed in combination with results from randomised trials. We focus on the effectiveness of tocilizumab and baricitinib in hospitalised patients. Result(s) This work will produce 2 key outputs. Firstly, an EHDEN Cancer Survival Dashboard that will allow HTA agencies to compare survival estimates received in evidence submissions with real-world survival data and projections. Secondly, novel insights into the comparison between 2 treatments for COVID-19, informed by the meta-analysis of non-randomised data from EHDEN and randomised data. Conclusion(s) Federated data networks like EHDEN could provide evidence-based healthcare decision makers with invaluable, rapid access to relevant RWE. The outputs from our use cases demonstrate potential opportunities. They also provide valuable tools to inform evidence gaps and reduce decision uncertainty in cancer and COVID-19 assessments, as exemplars. Copyright © 2022