Department of Health Sciences Research, Mayo Clinic, Scottsdale, AZ, USA.
Department of Radiation Oncology, Mayo Clinic, Phoenix, AZ, USA.
Qual Life Res. 2021 Nov;30(11):3189-3197. doi: 10.1007/s11136-020-02617-z. Epub 2020 Sep 9.
Tracking patient-reported outcomes (PROs) and quality-of-life response rates is essential for clinical trials. Historically, rates are monitored through scheduled reports, which can require gathering, merging, and cleaning data from multiple databases. At the end of this process, if gaps are found, new data are entered and the cycle repeats, leaving a trail of reports that are not up-to-date or immediately accessible to the investigator. The financial and person-hour cost of utilizing clinical research staff for this purpose is impractical. Online dashboards are continuously updated to monitor data, providing on-demand access to promote successful research.
Dashboard implementation utilizes R, an open-source statistical programming language, RMarkdown, a markup language, Flexdashboard, which creates structural elements, and Shiny, allowing investigators the ability to interact with data within the dashboard. By leveraging these four elements, powerful, cost-effective interactive dashboards can be built.
Numerous dashboards have been utilized to identify potentially missing data and increase protocol adherence. Immediate patient consultation can occur to retrieve protocol-related forms, reducing research staff and patient burden while improving trial effectiveness. Dashboards can monitor PROs, enrollment, demographics, toxicity, and biomarker data, clinical outcomes, and implemented predictive models, creating a single hub for on-demand clinical trial monitoring.
By employing a set of freely available tools, the burden of utilizing study staff to continuously monitor trials is greatly reduced. These tools allow users to rapidly build and deploy dynamic dashboards capable of meeting the research needs of any investigator while limiting missing data through simplified monitoring of protocol adherence.
跟踪患者报告的结果 (PRO) 和生活质量响应率对于临床试验至关重要。从历史上看，通过定期报告来监测这些指标，这可能需要从多个数据库中收集、合并和清理数据。在这个过程结束时，如果发现了差距，就会输入新的数据，然后重复这个周期，留下一系列报告，这些报告要么是过时的，要么是研究人员无法立即访问的。为了达到这个目的，利用临床研究人员的财务和人员时间成本是不切实际的。在线仪表板会不断更新以监控数据，提供按需访问权限，以促进成功的研究。
仪表板的实现利用了 R，一种开源的统计编程语言，RMarkdown，一种标记语言，Flexdashboard，它创建结构元素，以及 Shiny，允许研究人员在仪表板中与数据进行交互。通过利用这四个元素，可以构建功能强大、经济高效的交互式仪表板。
已经利用了许多仪表板来识别可能缺失的数据并提高协议的依从性。可以立即与患者进行协商以检索与方案相关的表格，从而减少研究人员和患者的负担，同时提高试验的效果。仪表板可以监测 PRO、入组、人口统计学、毒性和生物标志物数据、临床结果以及实施的预测模型，为按需临床试验监测创建一个单一的中心。
通过使用一组免费的工具，可以大大减轻利用研究人员持续监测试验的负担。这些工具允许用户快速构建和部署动态仪表板，能够满足任何研究人员的研究需求，同时通过简化对协议依从性的监控来限制数据缺失。
