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

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...

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Autores principales: Kasenda, Benjamin, Liu, Junhao, Jiang, Yu, Gajewski, Byron, Wu, Cen, von Elm, Erik, Schandelmaier, Stefan, Moffa, Giusi, Trelle, Sven, Schmitt, Andreas Michael, Herbrand, Amanda K., Gloy, Viktoria, Speich, Benjamin, Hopewell, Sally, Hemkens, Lars G., Sluka, Constantin, McGill, Kris, Meade, Maureen, Cook, Deborah, Lamontagne, Francois, Tréluyer, Jean-Marc, Haidich, Anna-Bettina, Ioannidis, John P. A., Treweek, Shaun, Briel, Matthias
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441612/
https://www.ncbi.nlm.nih.gov/pubmed/32825846
http://dx.doi.org/10.1186/s13063-020-04666-8
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author Kasenda, Benjamin
Liu, Junhao
Jiang, Yu
Gajewski, Byron
Wu, Cen
von Elm, Erik
Schandelmaier, Stefan
Moffa, Giusi
Trelle, Sven
Schmitt, Andreas Michael
Herbrand, Amanda K.
Gloy, Viktoria
Speich, Benjamin
Hopewell, Sally
Hemkens, Lars G.
Sluka, Constantin
McGill, Kris
Meade, Maureen
Cook, Deborah
Lamontagne, Francois
Tréluyer, Jean-Marc
Haidich, Anna-Bettina
Ioannidis, John P. A.
Treweek, Shaun
Briel, Matthias
author_facet Kasenda, Benjamin
Liu, Junhao
Jiang, Yu
Gajewski, Byron
Wu, Cen
von Elm, Erik
Schandelmaier, Stefan
Moffa, Giusi
Trelle, Sven
Schmitt, Andreas Michael
Herbrand, Amanda K.
Gloy, Viktoria
Speich, Benjamin
Hopewell, Sally
Hemkens, Lars G.
Sluka, Constantin
McGill, Kris
Meade, Maureen
Cook, Deborah
Lamontagne, Francois
Tréluyer, Jean-Marc
Haidich, Anna-Bettina
Ioannidis, John P. A.
Treweek, Shaun
Briel, Matthias
author_sort Kasenda, Benjamin
collection PubMed
description 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.
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spelling pubmed-74416122020-08-24 Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study Kasenda, Benjamin Liu, Junhao Jiang, Yu Gajewski, Byron Wu, Cen von Elm, Erik Schandelmaier, Stefan Moffa, Giusi Trelle, Sven Schmitt, Andreas Michael Herbrand, Amanda K. Gloy, Viktoria Speich, Benjamin Hopewell, Sally Hemkens, Lars G. Sluka, Constantin McGill, Kris Meade, Maureen Cook, Deborah Lamontagne, Francois Tréluyer, Jean-Marc Haidich, Anna-Bettina Ioannidis, John P. A. Treweek, Shaun Briel, Matthias Trials Study Protocol 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. BioMed Central 2020-08-21 /pmc/articles/PMC7441612/ /pubmed/32825846 http://dx.doi.org/10.1186/s13063-020-04666-8 Text en © The Author(s) 2020 Open AccessThis 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://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Study Protocol
Kasenda, Benjamin
Liu, Junhao
Jiang, Yu
Gajewski, Byron
Wu, Cen
von Elm, Erik
Schandelmaier, Stefan
Moffa, Giusi
Trelle, Sven
Schmitt, Andreas Michael
Herbrand, Amanda K.
Gloy, Viktoria
Speich, Benjamin
Hopewell, Sally
Hemkens, Lars G.
Sluka, Constantin
McGill, Kris
Meade, Maureen
Cook, Deborah
Lamontagne, Francois
Tréluyer, Jean-Marc
Haidich, Anna-Bettina
Ioannidis, John P. A.
Treweek, Shaun
Briel, Matthias
Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title_full Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title_fullStr Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title_full_unstemmed Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title_short Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)—rationale and design for an international collaborative study
title_sort prediction of recruitment in randomized clinical trials (recruit-it)—rationale and design for an international collaborative study
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7441612/
https://www.ncbi.nlm.nih.gov/pubmed/32825846
http://dx.doi.org/10.1186/s13063-020-04666-8
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