Imagine trying to prove a medicine works by only looking at a small, perfect group of people in a lab. This project looks at the 'messy' data from millions of real people using wearables and health records to see how drugs actually perform in daily life. It creates a rulebook so health authorities can trust this real-world evidence as much as they trust traditional clinical trials.
Traditional clinical trials are expensive and often fail to show how a drug performs in the diverse, real-world population. This creates a gap between trial efficacy and actual effectiveness, delaying regulatory approval and market access.
A three-stage fit-for-purpose assessment tool for databases, an online dashboard of 573 diabetes databases, and a set of normative standards for regulatory RWD use.
If you are a drug developer dealing with the high cost of long-term clinical trials — this project developed standards and a fit-for-purpose assessment tool that allows you to use real-world data to demonstrate efficacy and safety.
If you are a wearable manufacturer dealing with difficulty proving clinical value to regulators — this project developed a way to use data from devices and registries to bridge the gap between trial results and real-world effectiveness.
If you are an HTA body dealing with uncertainty about the value for money of new diabetes treatments — this project developed normative statements and modeling techniques to better assess the actual cost-effectiveness of interventions.
Based on available project data, no pricing or cost information for the resulting standards or tools is provided.
Yes, the project has already mapped 573 diabetes databases across 58 countries, indicating a high capacity for large-scale data application.
Based on available project data, specific IP or licensing terms are not mentioned; however, an online dashboard has already been launched for the research community.
By co-developing evidentiary standards with regulatory and HTA authorities, the project aims to make the use of real-world data more acceptable for decision making.
The project uses longitudinal targeted maximum likelihood estimation (TMLE) to integrate and analyze routine clinical data from registries in Denmark, Sweden, the UK, and Germany.
The consortium is heavily academic with 10 universities and 1 research institute, but maintains a 19% industry ratio with 3 industrial partners, including 2 SMEs. This mix suggests a strong scientific foundation for the modeling techniques, while the inclusion of partners from 9 countries (including the US and UK) ensures the resulting standards have international regulatory relevance.
Contact the Medizinische Universitat Graz for access to the RWD dashboard and fit-for-purpose assessment tool.
Contact SciTransfer to identify potential licensing opportunities for the REDDIE data assessment tools.
