Department of Biostatistics, University of Liverpool, A Member of Liverpool Health Partners, Liverpool, UK; Université de Paris, CRESS, INSERM, INRA, F-75004 Paris, France.
Department of Biostatistics, University of Liverpool, A Member of Liverpool Health Partners, Liverpool, UK.
J Clin Epidemiol. 2020 Aug;124:58-68. doi: 10.1016/j.jclinepi.2020.03.012. Epub 2020 Mar 27.
Identify the current practice for recruitment prediction and monitoring within clinical trials.
Chief investigators (CIs) were surveyed to identify data sources and adjustments made to support recruitment prediction. Statisticians were surveyed to determine methods and adjustments used when predicting and monitoring recruitment. Participants were identified from the National Institute for Health Research recently funded studies, the UK Clinical Research Collaboration registered Clinical Trial Units network or by the European Clinical Research Infrastructure Network.
A total of 51 CIs (UK = 32, ECRIN = 19) and 104 statisticians (UK = 51, ECRIN = 53) were contacted. Response rates varied (CIs UK = 53% ECRIN = 32%; statisticians UK = 98% ECRIN = 36%). Multiple data sources are used to support recruitment rates, most commonly audit data from multiple sites. Variation in individual site recruitment rates are frequently incorporated, but staggered site openings were featured more commonly among UK respondents. Simple prediction methods are preferred to rarely used statistical models. Lack of familiarity with statistical methods are barriers to their use with evidence needed to justify the time required to support their implementation.
Simplistic methods will continue as the mainstay of prediction; however, generation of evidence supporting the benefits of complex statistical models should promote their implementations. Multiple data sources to support recruitment prediction are being used, and further work on the quality of these data is needed. Pressure to be optimistic about recruitment rates for the trial to be attractive to funders was felt by a sizable minority.
确定临床试验中当前的招募预测和监测实践。
调查首席研究员（CIs）以确定支持招募预测的数据来源和调整。调查统计人员以确定预测和监测招募时使用的方法和调整。参与者是从英国国立卫生研究院最近资助的研究、英国临床研究协作注册的临床试验单位网络或欧洲临床研究基础设施网络中确定的。
共联系了 51 名首席研究员（英国 32 名，ECRIN 19 名）和 104 名统计学家（英国 51 名，ECRIN 53 名）。响应率各不相同（英国首席研究员 53%，ECRIN 32%；统计学家英国 98%，ECRIN 36%）。有多种数据来源可用于支持招募率，最常用的是来自多个站点的审计数据。经常纳入个体站点招募率的变化，但英国受访者更常采用分期站点开放。简单的预测方法比很少使用的统计模型更受欢迎。不熟悉统计方法是阻碍其使用的障碍，需要有证据证明支持其实施所需的时间是合理的。
简单的方法将继续作为预测的主要方法；但是，生成支持复杂统计模型益处的证据应该会促进它们的实施。正在使用多种数据来源来支持招募预测，并且需要进一步研究这些数据的质量。为了使试验对资助者有吸引力，有相当一部分人感到有压力要对招募率持乐观态度。
