Real-world evidence for decisions in diabetes (REDDIE)

随机对照试验是循证医学的基石。然而，现实世界数据(RWD)的数字化，包括来自设备、可穿戴设备和大型国家登记处的电子健康记录的数据，提供了展示创新技术的有效性和安全性的机会，这些创新技术包括药物、设备、诊断和数字健康。这些数据与糖尿病等长期疾病特别相关，在这些疾病中，药物、生活方式干预和数字技术经常协同工作。为了更好地将RWD用于糖尿病的调节决策，需要制定标准、指南和疗效与有效性差距的评估。Reddie(糖尿病决策的真实世界证据)旨在探索RWD如何补充随机对照试验，以提高预防和治疗糖尿病的技术的有效性、安全性和性价比。Reddie的总体目标是支持在糖尿病和健康相关研究中使用RWD，这将最大限度地发挥欧洲的科学专业知识和技术诀窍，使糖尿病患者受益，从而产生更安全、更有效和更具成本效益的干预措施。因此，我们的目标是与监管和HTA当局等利益相关者接触，共同制定RWD的收集、评估和可接受性的证据标准。然后，我们将使用从大型国家注册中心获得的合成数据来开发和验证最先进的建模技术，以更好地评估使用RWD进行干预的结果。我们将使用来自四个大型国家注册机构的数据来阐明随机对照试验和随机对照试验研究结果之间的差距，并了解影响这种差距的因素。最后，我们将测试机器学习的能力，以促进更好地使用RWD。Reddie将为RWD的使用制定标准，用于评估药物和监管机构和HTA机构的其他干预措施。

Randomised controlled trials 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 technologiesincluding 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, which will maximise Europe’s scientific expertise and know-how to benefit people with diabetes, resulting in safer, more efficient, and cost-effective interventions. We thus aim to engage with stakeholders such as regulatory and HTA authorities and co-develop evidentiary standards for the collection, assessment, and acceptability of RWD. We will then develop and validate state-of-the art modelling techniques using synthetic data derived from large national registries to better assess outcomes of interventions using RWD. We will use data from four large national registries to elucidate the gap between outcomes in RCTs and RWD studies, and understand the factors that affect this gap. Finally, we will test the ability of machine learning to facilitate the better use of RWD. REDDIE will generate standards for RWD use for the evaluation of medicines and other interventions by regulatory authorities and HTA bodies.