REDDIE is a ground-breaking initiative aimed at harnessing the potential of real-world data (RWD) to complement randomised controlled trials (RCTs) in diabetes research. While RCTs have long been the gold standard for evidence-based medicine, the digitization of RWD, including data from devices, wearables, and electronic health records, opens up new opportunities to demonstrate the efficacy and safety of innovative technologies in preventing and treating diabetes.
The project's primary objective is to explore how RWD can enhance the understanding of interventions for diabetes, leading to improved efficacy, safety, and cost-effectiveness. To achieve this, REDDIE seeks to engage with key stakeholders, such as regulatory and health technology assessment (HTA) authorities, to co-develop evidentiary standards for collecting, assessing, and accepting RWD. This collaboration aims to bridge the gap between RCTs and RWD studies and identify factors influencing this difference.
The consortium consists of 14 partners from 8 European countries, including experts in real-world data analysis, machine learning, health economics, clinical trials, and representatives from regulatory authorities, payers, HTA boards, and individuals with diabetes. By leveraging four large national registries, the project aims to develop state-of-the-art modeling techniques using synthetic data, which will enable policymakers to assess the impact of different variables on intervention outcomes.
Through the creation of "virtual trials," REDDIE intends to supplement and support future RCTs while evaluating the benefits of novel drugs, devices, and digital health interventions for diabetes. By setting up standards for RWD usage in assessing intervention efficacy, safety, and value for money, the project aims to benefit people with diabetes, ensuring safer, more efficient, and cost-effective treatments.
The project's innovative approach involves comparing outcomes from RCTs with matched and unmatched populations from RWD databases, enabling a deeper understanding of the efficacy to effectiveness gap and the contributing factors. This valuable insight will contribute to evidence-based decision-making and improve health outcomes for individuals living with diabetes.
