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 Lamontagne,
Jean-Marc Tréluyer,
Anna-Bettina Haidich,
John P. A. Ioannidis,
Shaun Treweek,
Matthias Briel
Affiliations
Benjamin Kasenda
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
Junhao Liu
Department of Biostatistics & Data Science, University of Kansas Medical Center
Yu Jiang
Division of Epidemiology, Biostatistics, and Environmental Health, School of Public Health, University of Memphis
Byron Gajewski
Department of Biostatistics & Data Science, University of Kansas Medical Center
Cen Wu
Department of Statistics, Kansas State University
Erik von Elm
Cochrane Switzerland, Center for Primary Care and Public Health (Unisanté), University of Lausanne
Stefan Schandelmaier
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
Giusi Moffa
Department of Mathematics and Computer Science, University of Basel
Sven Trelle
CTU Bern, University of Bern
Andreas Michael Schmitt
Department of Medical Oncology, University Hospital and University of Basel
Amanda K. Herbrand
Department of Medical Oncology, University Hospital and University of Basel
Viktoria Gloy
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
Benjamin Speich
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
Sally Hopewell
Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford
Lars G. Hemkens
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
Constantin Sluka
Clinical Trial Unit, Department of Clinical Research, University Hospital Basel and University of Basel
Kris McGill
Nursing, Midwifery, and Allied Health Professionals Research Unit, Glasgow Caledonian University
Maureen Meade
Department of Health Research Methods, Evidence, and Impact, McMaster University
Deborah Cook
Department of Health Research Methods, Evidence, and Impact, McMaster University
Francois Lamontagne
Centre de recherche du CHU de Sherbrooke and Université de Sherbrooke
Jean-Marc Tréluyer
Assistance Publique-Hopitaux de Paris, Hopitaux Universitaires Paris Centre, Unité de Recherche clinique
Anna-Bettina Haidich
Department of Hygiene, Social-Preventive Medicine & Medical Statistics, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki
John P. A. Ioannidis
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
Shaun Treweek
Health Services Research Unit, University of Aberdeen
Matthias Briel
Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel and University of Basel
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.
