The project Real4Reg is conducting a retrospective observational cohort study based on national healthcare registers and claims data. The project is developing methods for the effective analyses of real-world data (RWD) in regulatory decision-making and health technology assessment (HTA), by using emerging methods (artificial intelligence and machine learning (AI/ ML)).
The key steps of Real4Reg’s methodological work are:
- The description and understanding of the heterogeneity of RWD sources and patient characteristics. The datasets from all partners are being harmonised and standardised by developing a 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 more standardised way and enabling data FAIRification (Findable, Accessible, Interoperable, Reusable), via a metadata catalogue.
- The assessment of current analytical needs and the optimisation of the potential of the available methods help to increase the evidentiary value of RWD analyses. In addition, Real4Reg is focusing on the emerging opportunities of AI/ ML approaches to address current challenges in RWD analyses.
- In a final step, Real4Reg is deriving recommendations and developing guidance and training for the scientific and regulatory audiences. The results are being disseminated to patients and the general public to impact RWD use along the 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.
From a methodological point of view, the project is aligned with the decision-making process in regulatory agencies, 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 phenotypes were selected to develop and evaluate standards and data analytical approaches:
Use Case 1 (description of the study population) and Use Case 2 (historical controls and synthetic data) provide hands-on data experiences for the application of RWD in pre-authorisation and evaluation with phenotypes with high current regulatory and HTA interest: Breast cancer and Amyotrophic lateral sclerosis (ALS).
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 (FQs), 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: