An Online Clinical Trial Model with Secure Data Management System in the Implementation of Randomised Clinical Trials
| Autor: | Xiaoqian Liu, Sarah Robbins, Karen Schuck, David Hunter |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Journal of Clinical Rheumatology and Immunology, Vol 24, Iss supp01, Pp 43-44 (2024) |
| Druh dokumentu: | article |
| ISSN: | 26613417 2661-3425 2661-3417 |
| DOI: | 10.1142/S266134172474033X |
| Popis: | Background Nowadays, technological advances enable instant data collection in a secure and confidential manner. Integrating electronic data capture technologies into the online clinical trial design, has been rarely investigated. The aim of this study was to test the feasibility and logistics of the online clinical trial model in osteoarthritis. Methods We used Research Electronic Data Capture (REDCap), a versatile and secure database, as the data management platform. Without face-to-face interactions, the majority of the communications among investigators and between investigators and participants were through the REDCap platform. The embedded randomisation procedure was also completed on the same platform. Results We developed the online trial model in a 12-week, phase II, placebo-controlled, randomised clinical trial (RCT) investigating the efficacy and safety of a supplement combination in people with hand osteoarthritis (RADIANT study). We replicated the same platform and questionnaire model to a larger hybrid RCT, a phase III, two-year placebo-controlled trial investigating the use of stem cells in the treatment of knee osteoarthritis (SCUlpTOR study). In the RADIANT study, we screened 301 participants within five months’ timeframe between October 2019 and March 2020 during the pandemic of COVID. 106 eligible participants were randomised, and the adherence rate was 100%. By leveraging advanced REDCap features, including custom record status dashboards, automated alerts and notifications, text messaging integration via Twilio, and comprehensive project dashboards, we significantly enhanced the efficiency of the recruitment process and participant management. These improvements also effectively reduced the survey burden on participants, contributing to the overall success and robustness of our data collection methodology. In the SCUlpTOR study, we successfully completed the recruitment between May 2021 to Nov 2023 in two sites with screening of 10,110 participants and eventually 321 participants were randomised. The visualisation of the process provides an efficient system to adjust the recruitment strategy in a cost-effective and efficient manner (Figure 1). Conclusion This online trial implementation and data management system successfully work for RCT recruitment and conduction. The two well-performed trials prove the feasibility of online clinic trial model. This is particularly important in the post-COVID era when face-to-face interactions may be limited. However, the incorporation of technology and secure data management system in the implementation of RCT is still underexplored. Further validation is needed to generalize this method to a broader trial design in other medical disorders. |
| Databáze: | Directory of Open Access Journals |
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