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


Journal

Trials
ISSN: 1745-6215
Titre abrégé: Trials
Pays: England
ID NLM: 101263253

Informations de publication

Date de publication:
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

731

Subventions

Organisme : Chief Scientist Office
ID : HSRU1
Pays : United Kingdom
Organisme : Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
ID : 320030_133540/1

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Auteurs

Benjamin Kasenda (B)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland. benjamin.kasenda@gmail.com.
Department of Medical Oncology, University Hospital and University of Basel, Basel, Switzerland. benjamin.kasenda@gmail.com.

Junhao Liu (J)

Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City, USA.

Yu Jiang (Y)

Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis, Memphis, TN, 38152, USA.

Byron Gajewski (B)

Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, KS, 66160, USA.
University of Kansas Cancer Center, University of Kansas Medical Center, Kansas City, USA.

Cen Wu (C)

Department of Statistics, Kansas State University, Manhattan, KS, 66506, USA.

Erik von Elm (E)

Cochrane Switzerland, Center for Primary Care and Public Health (Unisanté), University of Lausanne, Lausanne, Switzerland.

Stefan Schandelmaier (S)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.

Giusi Moffa (G)

Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland.

Sven Trelle (S)

CTU Bern, University of Bern, Bern, Switzerland.

Andreas Michael Schmitt (AM)

Department of Medical Oncology, University Hospital and University of Basel, Basel, Switzerland.

Amanda K Herbrand (AK)

Department of Medical Oncology, University Hospital and University of Basel, Basel, Switzerland.

Viktoria Gloy (V)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.

Benjamin Speich (B)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Sally Hopewell (S)

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK.

Lars G Hemkens (LG)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.

Constantin Sluka (C)

Clinical Trial Unit, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.

Kris McGill (K)

Nursing, Midwifery, and Allied Health Professionals Research Unit, Glasgow Caledonian University, Glasgow, UK.

Maureen Meade (M)

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.

Deborah Cook (D)

Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.

Francois Lamontagne (F)

Centre de recherche du CHU de Sherbrooke and Université de Sherbrooke, Sherbrooke, Canada.

Jean-Marc Tréluyer (JM)

Assistance Publique-Hopitaux de Paris, Hopitaux Universitaires Paris Centre, Unité de Recherche clinique, 27 rue du Faubourg Saint-Jacques, 75014, Paris, France.

Anna-Bettina Haidich (AB)

Department of Hygiene, Social-Preventive Medicine & Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece.

John P A Ioannidis (JPA)

Meta-Research Innovation Center at Stanford (METRICS) and Departments of Medicine, of Health Research and Policy, of Biomedical Data Science, and of Statistics, Stanford University, Stanford, USA.

Shaun Treweek (S)

Health Services Research Unit, University of Aberdeen, Aberdeen, UK.

Matthias Briel (M)

Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland.
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada.

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Classifications MeSH