| Literature DB >> 36011793 |
Friedemann Schad1,2, Anja Thronicke2.
Abstract
Real-world evidence (RWE) is increasingly involved in the early benefit assessment of medicinal drugs. It is expected that RWE will help to speed up approval processes comparable to RWE developments in vaccine research during the COVID-19 pandemic. Definitions of RWE are diverse, marking the highly fluid status in this field. So far, RWE comprises information produced from data routinely collected on patient's health status and/or delivery of health care from various sources other than traditional clinical trials. These sources can include electronic health records, claims, patient-generated data including in home-use settings, data from mobile devices, as well as patient, product, and disease registries. The aim of the present update was to review current RWE developments and guidelines, mainly in the U.S. and Europe over the last decade. RWE has already been included in various approval procedures of regulatory authorities, reflecting its actual acceptance and growing importance in evaluating and accelerating new therapies. However, since RWE research is still in a transition process, and since a number of gaps in this field have been explored, more guidance and a consented definition are necessary to increase the implementation of real-world data.Entities:
Keywords: digital health technology; early drug approval; randomized controlled trials; real-world data; real-world evidence; registry; review
Mesh:
Year: 2022 PMID: 36011793 PMCID: PMC9408280 DOI: 10.3390/ijerph191610159
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
Figure 1Sources of real-world evidence (RWE) research.
RWE developments in the international context for regulatory decision-making.
| RWE Developments | Year |
|---|---|
| FDA—21st Century Cures Act [ | 2016 |
| FDA—Use of RWE to Support Regulatory Decision-Making for Medical Devices [ | 2017 |
| FDA—Use of Electronic Health Record Data in Clinical Investigations Guidance for Industry [ | 2018 |
| FDA—Framework for FDA’s real-world evidence program [ | 2018 |
| National Institute for Health and Care Excellence (NICE)—Evidence standards framework for digital health technologies [ | 2018 |
| FDA—Submitting Documents Using RWD and RWE to FDA for Drugs and Biologics Guidance for Industry [ | 2018 |
| HMA-EMA Joint Big Data Taskforce Phase II report: Evolving Data-Driven Regulation [ | 2019 |
| ISPE’s Comments on the Core Recommendations in the Summary of the Heads of Medicines Agencies (HMA)—EMA Joint Big Data Task Force [ | 2019 |
| EMA – Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products [ | 2019 |
| Federal Joint Committee (G-BA) (Germany) and the German Institute for Quality and Efficiency in Health Care (IQWIG): Scientific rapid report on the evaluation of healthcare research for the purpose of the benefit assessment of drugs [ | 2019 |
| RWE taskforsk of the ISPE—Statement on Real-World Evidence (RWE) [ | 2020 |
| EMA—guidance on registry-based studies as a tool to generate RWE for marketing authorization applicants and holder [ | 2020 |
| FDA—grants for 4 projects exploring the utility of RWE [ | 2020 |
| Medicines and healthcare products regulatory agency—Guidance on randomised controlled trials generating real-world evidence to support regulatory decisions [ | 2020 |
| RAPS Euro Convergence—conference created by European regulatory professionals for regulatory professionals operating in Europe and other countries [ | 2021 |
| FDA—Considerations for the Use of RWD and RWE to Support Regulatory Decision-Making for Drug and Biological Products, guidance for industry, draft guidance [ | 2021 |
Figure 2Challenges in RWE research.
Figure 3Potentials of RWE studies.
FDA-funded RWE research projects for the utility of RWE.
| RWE Developments | Institution |
|---|---|
| Enhancing evidence generation by linking randomized clinical trials (RCTs) to real-world data (RWD) [ | Brigham and Women’s Hospital, Harvard Medical School, U.S. |
| Applying novel statistical approaches to develop a decision framework for hybrid randomized controlled trial designs which combine internal control arms with patients’ data from real-world data source [ | University of North Carolina, Genentech, Inc., U.S. |
| Advancing standards and methodologies to generate real-world evidence from real-world data through a neonatal pilot [ | Critical Path Institute (C-Path), Tufts Medical Center, International Neonatal Consortium, U.S. |
| Transforming real-world evidence with unstructured and structured data to advance tailored therapy (TRUST) [ | Verantos, U.S. |