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

Benjamin Kasenda*, Junhao Liu, Yu Jiang, Byron Gajewski, Cen Wu, Erik von Elm, Stefan Schandelmaier, Giusi Moffa, Sven Trelle, Andreas Michael Schmitt, Amanda K. Herbrand, Viktoria Gloy, Benjamin Speich, Sally Hopewell, Lars G Hemkens, Constantin Sluka, Kris McGill, Maureen Meade, Deborah Cook, Francois LamontagneJean-Marc Tréluyer, Anna-Bettina Haidich, John P. A. Ioannidis, Shaun Treweek, Matthias Briel

*Corresponding author for this work

Research output: Contribution to journalArticle

Abstract

Background:
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.
Methods:
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.
Discussion:
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.
Original languageEnglish
Article number731
Number of pages7
JournalTrials
Volume21
DOIs
Publication statusPublished - 21 Aug 2020

Keywords

  • Recruitment
  • Accrua
  • Prediction
  • Randomized clinical trials
  • Accrual
  • PATIENT ENROLLMENT
  • PUBLICATION
  • INCREASING VALUE
  • TIME
  • FATE
  • REDUCING WASTE

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    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., ... Briel, M. (2020). Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—: —rationale and design for an international collaborative study. Trials, 21, [731]. https://doi.org/10.1186/s13063-020-04666-8