Population and Behavioural Science Division, School of Medicine, Medical & Biological Sciences, North Haugh, St Andrews, UK.
Information Services, University of Glasgow, Hillhead Street, Glasgow, G12 8QE, UK.
Trials. 2019 May 10;20(1):266. doi: 10.1186/s13063-019-3354-z.
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.
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.
PROSPERO, CRD42018100695 . Registered on 03 July 2018.
随机对照试验(RCT)经常无法招募到足够数量的参与者。这会影响试验回答提出的研究问题的能力,浪费资源,并且可能不道德。在一般实践环境中进行的 RCT 越来越常见,同样面临着招募挑战。拟议综述的目的是确定与一般实践环境中 RCT 招募率相关的因素。这些结果将用于进一步的研究,以预测 RCT 的招募情况。
方法/设计:将对 Medline、EMBASE、Cochrane 系统评价数据库、NTIS 和 OpenGrey 等电子数据库进行搜索,不限制发表日期。过去 5 年将手动搜索 BMC Trials。如果研究涉及一般实践 RCT 中的招募情况,则将纳入定量和定性研究。仅纳入英文出版物。两名审查作者将对筛选、质量评估和数据提取进行盲法审查。如果有分歧,将通过讨论解决,并在需要时请第三名审查作者参与。将对纳入的研究进行叙述性综合。
该综述将首次系统地综合现有的关于一般实践中与 RCT 招募率相关因素的研究。通过确定进一步研究中需要优先考虑的研究空白,它将引起学术界的兴趣。它也将对参与提高试验招募这一复杂任务的临床试验人员有价值。我们的团队将利用这些发现来告知使用机器学习的试验招募预测模型。
PROSPERO,CRD42018100695。于 2018 年 7 月 3 日注册。