Factors associated with recruitment to randomised controlled trials in general practice: protocol for a systematic review

Moffat, K., Cannon, P. , Shi, W. and Sullivan, F. (2019) Factors associated with recruitment to randomised controlled trials in general practice: protocol for a systematic review. Trials, 20, 266. (doi: 10.1186/s13063-019-3354-z) (PMID:31077231) (PMCID:PMC6511135)

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Background: Randomised controlled trials (RCTs) are frequently unable to recruit sufficient numbers of participants. This affects the trial’s ability to answer the proposed research question, wastes resources and can be unethical. RCTs within a general practice setting are increasingly common and similarly face recruitment challenges. The aim of the proposed review is to identify factors that are associated with recruitment rates to RCTs in a general practice setting. These results will be used in further research to predict recruitment to RCTs. Methods/design: The electronic databases Medline, EMBASE, Cochrane Database of Systematic Reviews, NTIS and OpenGrey will be searched for relevant articles with no limit on the date of publication. BMC Trials will be manually searched for the past 5 years. Both quantitative and qualitative studies will be included if they have studied recruitment within a general practice RCT. Only English language publications will be included. Screening, quality assessment and data extraction will be conducted by two review authors not blinded to study characteristics. Disagreement will be resolved by discussion and the involvement of a third review author if required. A narrative synthesis of the studies included will be performed. Discussion: The review will, for the first time, systematically synthesise existing research on factors associated with recruitment rates to RCTs in general practice. By identifying research gaps to be prioritised in further research, it will be of interest to academics. It will also be of value to clinical trialists who are involved in the complex task of improving trial recruitment. Our team will use the findings to inform a prediction model of trial recruitment using machine learning. Systematic review registration: PROSPERO, CRD42018100695. Registered on 03 July 2018.

Item Type:Articles
Additional Information:This study is supported by a PhD studentship provided by the University of St Andrews.
Glasgow Author(s) Enlighten ID:Cannon, Mr Paul and Moffat, Dr Keith
Authors: Moffat, K., Cannon, P., Shi, W., and Sullivan, F.
College/School:College of Medical Veterinary and Life Sciences > Institute of Health and Wellbeing > General Practice and Primary Care
University Services > Library and Collection Services > Library
Journal Name:Trials
Publisher:BioMed Central
ISSN (Online):1745-6215
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Trials 20: 266
Publisher Policy:Reproduced under a Creative Commons License

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