Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study.
Accrual
Prediction
Randomized clinical trials
Recruitment
Journal
Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253
Informations de publication
Date de publication:
21 Aug 2020
21 Aug 2020
Historique:
received:
08
12
2019
accepted:
09
08
2020
entrez:
23
8
2020
pubmed:
23
8
2020
medline:
28
5
2021
Statut:
epublish
Résumé
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.
Sections du résumé
BACKGROUND
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
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
CONCLUSIONS
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.
Identifiants
pubmed: 32825846
doi: 10.1186/s13063-020-04666-8
pii: 10.1186/s13063-020-04666-8
pmc: PMC7441612
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
731Subventions
Organisme : Chief Scientist Office
ID : HSRU1
Pays : United Kingdom
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 320030_133540/1
Références
Contemp Clin Trials. 2015 Nov;45(Pt A):26-33
pubmed: 26188165
J Clin Epidemiol. 2019 Nov;115:141-149
pubmed: 31299358
Stat Med. 2015 Feb 20;34(4):613-29
pubmed: 25376910
J Clin Epidemiol. 2001 Sep;54(9):877-83
pubmed: 11520646
Lancet. 2014 Jan 11;383(9912):166-75
pubmed: 24411645
Trials. 2009 Jul 10;10:52
pubmed: 19591685
Am J Epidemiol. 2001 Nov 1;154(9):873-80
pubmed: 11682370
Lancet. 2014 Jan 11;383(9912):176-85
pubmed: 24411646
BMJ. 2005 Jul 2;331(7507):19
pubmed: 15967761
JAMA. 2014 Mar 12;311(10):1045-51
pubmed: 24618966
BMJ. 2014 Dec 10;349:g7089
pubmed: 25499097
Biometrics. 1986 Sep;42(3):507-19
pubmed: 3567285
BMC Med Res Methodol. 2010 Jul 06;10:63
pubmed: 20604946
Trials. 2016 Jul 22;17(1):336
pubmed: 27449769
Clin Trials. 2019 Dec;16(6):657-664
pubmed: 31451012
Health Technol Assess. 2007 Nov;11(48):iii, ix-105
pubmed: 17999843
BMC Med Res Methodol. 2001;1:4
pubmed: 11423002
BMC Med Res Methodol. 2012 Aug 28;12:131
pubmed: 22928744
Trials. 2015 Jun 05;16:261
pubmed: 26044814
Contemp Clin Trials. 2018 Mar;66:74-79
pubmed: 29330082
J R Soc Med. 1992 Feb;85(2):71-6
pubmed: 1538384