The project Real4Reg encompasses a historical cohort study based on national healthcare registers and claims data from Denmark, Finland, Germany, and Portugal. The project is developing methods for analysing real-world data (RWD) in regulatory decision-making and health technology assessment (HTA), by enhancing and extending state-of-the art approaches with novel artificial intelligence and machine learning (AI/ ML).
The key steps of Real4Reg’s methodological work are:
- Describe diversity and heterogeneity of the RWD sources and patient populations. Country-specific datasets are being harmonised using the OMOP common data model (CDM) and analytical workflows, which can be employed in future projects. As a result, Real4Reg is enabling the use of different RWD in a standardised way and enabling data FAIRification (Findable, Accessible, Interoperable, Reusable), via a metadata catalogue.
- Assess analytical needs and the optimise the available methods help to increase the evidentiary value of RWD-based analyses. In addition, Real4Reg is focusing on the emerging opportunities of AI/ ML approaches to address current challenges in RWD analyses.
- Deriving recommendations and develop guidance and training for regulatory, HTA, academia, industry, payers, and patient representatives. The results are being disseminated to patients and the general public to enable RWD use along the entire product lifecycle.
ENCePP Study Seal
The Real4Reg study was registered in the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP) database.
As the Real4Reg study meets the rigorous criteria set by ENCePP’s Code of Conduct, it has received an ENCePP Seal.
Methodologically, the project is aligned with the regulatory decision-making process, which can be broadly broken down into the pre-authorisation, evaluation, and post-authorisation phases of the product lifecycle.
Four use cases and corresponding suitable patient phenotypes were selected to develop and evaluate standards and data analytical approaches:
Use Case 1 (description of the study population) and Use Case 2 (external controls and synthetic data) are hands-on prood-of-concept exercises of application of RWD in pre-authorisation and evaluation, using two patient phenotypes that have high regulatory and HTA interest and simultaneously represent diseases that differ basic epidemiology, frequency, and clinical course: breast cancer and amyotrophic lateral sclerosis.
Real4Reg also provides hands-on data experiences for phenotypes with current regulatory interest from the post-authorisation perspective. These phenotypes are complementary as they address treatments for both acute and chronic conditions: fluoroquinolones, a class of broad-spectrum antibiotics, were chosen to evaluate safety as Use Case 3. Use Case 4 is evaluating the effectiveness and drug repurposing of SGLT2 inhibitors, combining two major public health concerns: diabetes and heart failure.
The following image shows an overview of the methodology: