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

Keith R. MoffatEmail author View ORCID ID profile,
Paul CannonView ORCID ID profile,
Wen Shi and
Frank Sullivan

Trials201920:266

https://doi.org/10.1186/s13063-019-3354-z

Received: 9 August 2018
Accepted: 3 April 2019
Published: 10 May 2019

Open Peer Review reports

Abstract

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.