Department of Industrial Engineering, Stellenbosch University, Joubert Street, Stellenbosch, 7600, South Africa.
Alcohol, Tobacco and Other Drug Research Unit, South African Medical Research Council, Francie van Zyl Drive, Tygerberg, Cape Town, 7505, South Africa.
Trials. 2021 Mar 6;22(1):189. doi: 10.1186/s13063-021-05112-z.
Predicting and monitoring recruitment in large, complex trials is essential to ensure appropriate resource management and budgeting. In a novel partnership between clinical trial investigators of the South African Medical Research Council and industrial engineers from the Stellenbosch University Health Systems Engineering and Innovation Hub, we developed a trial recruitment tool (TRT). The objective of the tool is to serve as a computerised decisions-support system to aid the planning and management phases of the trial recruitment process.
The specific requirements of the TRT were determined in several workshops between the partners. A Poisson process simulation model was formulated and incorporated in the TRT to predict the recruitment duration. The assumptions underlying the model were made in consultation with the trial team at the start of the project and were deemed reasonable. Real-world data extracted from a current cluster trial, Project MIND, based in 24 sites in South Africa was used to verify the simulation model and to develop the monitoring component of the TRT.
The TRT comprises a planning and monitoring component. The planning component generates different trial scenarios for predicted trial recruitment duration based on user inputs, e.g. number of sites, initiation delays. The monitoring component uses and analyses the data retrieved from the trial management information system to generate different levels of information, displayed visually on an interactive, user-friendly dashboard. Users can analyse the results at trial or site level, changing input parameters to see the resultant effect on the duration of trial recruitment.
This TRT is an easy-to-use tool that assists in the management of the trial recruitment process. The TRT has potential to expedite improved management of clinical trials by providing the appropriate information needed for the planning and monitoring of the trial recruitment phase. This TRT extends prior tools describing historic recruitment only to using historic data to predict future recruitment. The broader project demonstrates the value of collaboration between clinicians and engineers to optimise their respective skillsets.
在大型、复杂的试验中预测和监测招募情况对于确保适当的资源管理和预算编制至关重要。在南非医学研究理事会的临床试验研究人员与斯坦陵布什大学卫生系统工程与创新中心的工业工程师之间的一项新合作中，我们开发了一种试验招募工具（TRT）。该工具的目的是作为一个计算机化的决策支持系统，辅助试验招募过程的规划和管理阶段。
在合作伙伴之间的多次研讨会上确定了 TRT 的具体要求。制定了泊松过程仿真模型并将其纳入 TRT 中，以预测招募持续时间。在项目开始时与试验团队进行了协商，对模型的假设进行了评估，认为其是合理的。从南非 24 个地点开展的当前集群试验“项目 MIND”中提取的真实数据用于验证仿真模型并开发 TRT 的监测组件。
TRT 包括规划和监测组件。规划组件根据用户输入（例如站点数量、启动延迟）生成不同的试验场景，用于预测试验招募持续时间。监测组件使用并分析从试验管理信息系统中检索的数据，生成不同级别的信息，以交互式、用户友好的仪表板形式直观显示。用户可以在试验或站点级别分析结果，更改输入参数，以查看其对试验招募持续时间的影响。
TRT 是一个易于使用的工具，可协助管理试验招募过程。TRT 有可能通过提供规划和监测试验招募阶段所需的适当信息，加速对临床试验的改进管理。该 TRT 扩展了先前仅描述历史招募的工具，以利用历史数据预测未来的招募情况。该更广泛的项目展示了临床医生和工程师之间合作的价值，以优化各自的技能。
