Periodic Reporting for period 1 - REDDIE (Real-world evidence for decisions in diabetes)
Reporting period: 2023-01-01 to 2024-06-30
Randomised controlled trials (RCT) are the cornerstone of evidence-based medicine. However, the digitisation of real-world data (RWD) including data from devices, wearables, and electronic health records in large national registries provides opportunities to demonstrate efficacy and safety of innovative technologies including drugs, devices, diagnostics, and digital health. These data are particularly relevant to long-term conditions such as diabetes mellitus, where drugs, lifestyle interventions, and digital technologies often work together.
To better utilise RWD in diabetes for regulatory decision making, a development of standards, guidance, and an assessment of the efficacy to effectiveness gap is needed.
REDDIE (Real-World Evidence for Decisions in Diabetes) aims to explore how RWD can complement RCTs to improve efficacy, safety, and value for money of technologies to prevent and treat diabetes. The overall aim of REDDIE is to support the use of RWD in diabetes and health-related research.
To better utilise RWD in diabetes for regulatory decision making, a development of standards, guidance, and an assessment of the efficacy to effectiveness gap is needed.
REDDIE (Real-World Evidence for Decisions in Diabetes) aims to explore how RWD can complement RCTs to improve efficacy, safety, and value for money of technologies to prevent and treat diabetes. The overall aim of REDDIE is to support the use of RWD in diabetes and health-related research.
We are engaging with stakeholders such as regulatory and HTA authorities in order to co-develop evidentiary standards for the collection, assessment, and acceptability of RWD.
In order to investigate the efficacy to effectiveness gap of glucose-lowering pharmacological therapies in diabetes, we are looking at how models derived from RCT data perform within RWD from patients that have received the same intervention as RCT participants. These RWD data are derived from databases originating from population-level registers and databases from four countries, including the Danish National Administrative Registers, the German Diabetes-Patienten-Verlaufsdokumentation (DPV), the Swedish National Diabetes Register (NDR) and the UK National Diabetes Audit (NDA).
Specifically, we have already published analysis plans and are presently working on an analysis of the following models in RWD:
1. GLP-1 vs. DPP-4 (LEADER inspired population; carried out in DK, UK, SE)
2. SGLT-2 vs. DPP-4 (EMPA-REG inspired population; carried out in DK, UK, SE)
3. Insulin degludec vs. glargine (DEVOTE inspired population; carried out in DK)
4. DPP-4 vs. SU (TECOS inspired population; carried out in DK)
5. GLP-1 vs. DPP-4 as second-line therapy (LEAD2 inspired population; carried out in DK, GE)
6. GLP-1 receptor agonists vs. metformin as first-line therapy (LEAD1 inspired population, carried out in DK, GE, SE)
In order to facilitate a better use of RWD, we will also
a) develop and validate modelling techniques using synthetic data derived from these registries and
b) building simulation models based on machine learning (Bayesian networks) for virtual trials in diabetes research
In order to investigate the efficacy to effectiveness gap of glucose-lowering pharmacological therapies in diabetes, we are looking at how models derived from RCT data perform within RWD from patients that have received the same intervention as RCT participants. These RWD data are derived from databases originating from population-level registers and databases from four countries, including the Danish National Administrative Registers, the German Diabetes-Patienten-Verlaufsdokumentation (DPV), the Swedish National Diabetes Register (NDR) and the UK National Diabetes Audit (NDA).
Specifically, we have already published analysis plans and are presently working on an analysis of the following models in RWD:
1. GLP-1 vs. DPP-4 (LEADER inspired population; carried out in DK, UK, SE)
2. SGLT-2 vs. DPP-4 (EMPA-REG inspired population; carried out in DK, UK, SE)
3. Insulin degludec vs. glargine (DEVOTE inspired population; carried out in DK)
4. DPP-4 vs. SU (TECOS inspired population; carried out in DK)
5. GLP-1 vs. DPP-4 as second-line therapy (LEAD2 inspired population; carried out in DK, GE)
6. GLP-1 receptor agonists vs. metformin as first-line therapy (LEAD1 inspired population, carried out in DK, GE, SE)
In order to facilitate a better use of RWD, we will also
a) develop and validate modelling techniques using synthetic data derived from these registries and
b) building simulation models based on machine learning (Bayesian networks) for virtual trials in diabetes research
Successfully developing and implementing the described new methods and evidentiary standards for RWD for regulatory and HTA decision-making has the potential to get new technologies to patients sooner and with fewer RCTs, which will have a significant effect on R&D cost.
Our development of modelling techniques using synthetic data as well as our simulation models based on machine learning will improve the conduction of retrospective observational studies based on RWD in scenarios where automation and scalability are important. Also, the enhanced bnstruct library developed in WP5 will provide researchers with an enhanced software to build DBN simulation models from RWD, facilitating the development of new simulation models and their use for in silico trials.
Our development of modelling techniques using synthetic data as well as our simulation models based on machine learning will improve the conduction of retrospective observational studies based on RWD in scenarios where automation and scalability are important. Also, the enhanced bnstruct library developed in WP5 will provide researchers with an enhanced software to build DBN simulation models from RWD, facilitating the development of new simulation models and their use for in silico trials.