Prediction of RECRUITment In Randomized Clinical Trials (RECRUIT-IT) - Rationale and Design for an International Collaborative Study

Kasenda, Benjamin and Liu, Junhao and Jiang, Yu and Gajewski, Byron and Wu, Cen and von Elm, Erik and Schandelmaier, Stefan and Moffa, Giusi and Trelle, Sven and Schmitt, Andreas Michael and Herbrand, Amanda K. and Gloy, Viktoria and Speich, Benjamin and Hopewell, Sally and Hemkens, Lars G. and Sluka, Constantin and McGill, Kris and Meade, Maureen and Cook, Deborah and Lamontagne, Francois and Tréluyer, Jean-Marc and Haidich, Anna-Bettina and Ioannidis, John P. A. and Treweek, Shaun and Briel, Matthias. (2020) Prediction of RECRUITment In Randomized Clinical Trials (RECRUIT-IT) - Rationale and Design for an International Collaborative Study. Trials, 21. p. 731.

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Official URL: https://edoc.unibas.ch/81028/

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Poor recruitment of patients is the predominant reason for early termination of randomized clinical trials (RCTs). Systematic empirical investigations and validation studies of existing recruitment models, however, are lacking. We aim to provide evidence-based guidance on how to predict and monitor recruitment of patients into RCTs. Our specific objectives are the following: (1) to establish a large sample of RCTs (target n = 300) with individual patient recruitment data from a large variety of RCTs, (2) to investigate participant recruitment patterns and study site recruitment patterns and their association with the overall recruitment process, (3) to investigate the validity of a freely available recruitment model, and (4) to develop a user-friendly tool to assist trial investigators in the planning and monitoring of the recruitment process.
Eligible RCTs need to have completed the recruitment process, used a parallel group design, and investigated any healthcare intervention where participants had the free choice to participate. To establish the planned sample of RCTs, we will use our contacts to national and international RCT networks, clinical trial units, and individual trial investigators. From included RCTs, we will collect patient-level information (date of randomization), site-level information (date of trial site activation), and trial-level information (target sample size). We will examine recruitment patterns using recruitment trajectories and stratifications by RCT characteristics. We will investigate associations of early recruitment patterns with overall recruitment by correlation and multivariable regression. To examine the validity of a freely available Bayesian prediction model, we will compare model predictions to collected empirical data of included RCTs. Finally, we will user-test any promising tool using qualitative methods for further tool improvement.
This research will contribute to a better understanding of participant recruitment to RCTs, which could enhance efficiency and reduce the waste of resources in clinical research with a comprehensive, concerted, international effort.
Faculties and Departments:05 Faculty of Science > Departement Mathematik und Informatik > Mathematik > Statistik (Moffa)
UniBasel Contributors:Moffa, Giusi
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:BioMed Central
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 Apr 2021 13:41
Deposited On:13 Apr 2021 13:41

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