Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study.

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State: Public
Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_74D593438D86
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
Collection
Publications
Institution
Title
Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study.
Journal
Trials
Author(s)
Kasenda B., Liu J., Jiang Y., Gajewski B., Wu C., von Elm E., Schandelmaier S., Moffa G., Trelle S., Schmitt A.M., Herbrand A.K., Gloy V., Speich B., Hopewell S., Hemkens L.G., Sluka C., McGill K., Meade M., Cook D., Lamontagne F., Tréluyer J.M., Haidich A.B., Ioannidis JPA, Treweek S., Briel M.
ISSN
1745-6215 (Electronic)
ISSN-L
1745-6215
Publication state
Published
Issued date
21/08/2020
Peer-reviewed
Oui
Volume
21
Number
1
Pages
731
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
Abstract
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.
Keywords
Humans, Patient Selection, Randomized Controlled Trials as Topic, Research Design, Research Personnel, Sample Size, Accrual, Prediction, Randomized clinical trials, Recruitment
Pubmed
Web of science
Open Access
Yes
Create date
31/08/2020 15:23
Last modification date
08/08/2024 7:35
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