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Title: | Multi-stakeholder sessions on major innovation topics in rare disease clinical trials | Authors: | Bodden, Daniel Schoenen, Stefanie Wied, Stephanie VERBEECK, Johan Dirani, Maya Daya, Hiba Abou Heussen, Nicole MOLENBERGHS, Geert Hilgers, Ralf-Dieter Nabbout, Rima |
Issue Date: | 2024 | Publisher: | BMC | Source: | Orphanet journal of rare diseases, 19 (1) (Art N° 467) | Abstract: | BackgroundThe European Joint Programme on Rare Diseases aims to enhance the rare diseases research ecosystem by bringing together stakeholders such as research funders, institutions and patient organizations. Work Package 20 focuses on the validation, use and development of innovative methodologies for rare disease clinical trials. This paper reports on the outcomes of a retreat held in April 2023, where areas for innovation and educational needs in rare disease clinical trials were discussed in multi-stakeholder sessions.MethodsMulti-stakeholder sessions covered the topics: Future Educational System, Randomization in Rare Disease Clinical Trials, Endpoints in Rare Disease Clinical Trials and Using History Course Data. The sessions began with expert presentations to set the scene, followed by guided discussions facilitated by questions on a collaborative digital whiteboard. Participants wrote responses, which were then discussed live with the experts.ResultsTraining is needed for diverse stakeholders in rare disease clinical trials to enhance understanding and drive innovation. Challenges include a lack of standardized terminology for multiple endpoints, inadequate understanding of randomization in small sample studies and various obstacles in effectively using natural history data.ConclusionCreating a comprehensive and sustainable educational program for rare diseases clinical trial methodology requires strategic collaboration and adherence to FAIR principles. The workshop highlighted the need for innovations for topics in areas such as handling missing data, optimizing the extraction of information from small samples, remote endpoint measurement and new randomized inference techniques. Additionally, integrating innovations into tailored training programs is crucial for advancing the field. | Notes: | Bodden, D (corresponding author), RWTH Aachen Univ Aachen, Inst Med Stat, Pauwelsstr 19, D-52064 Aachen, Germany. daniel.bodden@rwth-aachen.de; stschoenen@ukaachen.de; swied@ukaachen.de; johan.verbeeck@uhasselt.be; dr.mayadirani@gmail.com; Hibaaboudaya@hotmail.com; nicole.heussen@med.sfu.ac.at; geert.molenberghs@uhasselt.be; rhilgers@ukaachen.de; rima.nabbout@aphp.fr |
Keywords: | Finite populations;Natural history;Randomization;Multiple endpoints;Educational system | Document URI: | http://hdl.handle.net/1942/45015 | e-ISSN: | 1750-1172 | DOI: | 10.1186/s13023-024-03482-6 | ISI #: | 001380845700005 | Rights: | The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑ mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
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Multi-stakeholder sessions on major innovation topics in rare disease clinical trials.pdf | Published version | 1.27 MB | Adobe PDF | View/Open |
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