在顺序多重分配随机试验 (SMART) 中使用研究电子数据捕获 (REDCap):自动化双随机化的实用例子
Carol A Lee1, Danilo Gamino2, Michelle Lore3
1Addiction Center, University of Michigan, North Campus Research Complex Building 16, 2800 Plymouth Rd., Room 222W, Ann Arbor, MI, 48109, USA. carolal@med.umich.edu.
BMC medical research methodology
|July 6, 2023
概括
研究人员可以使用REDCap,一个数据捕获系统,为顺序多重分配随机试验 (SMART) 自动化双随机化. 这一策略提高了适应性干预研究的严格性,并减少了潜在的错误.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 临床试验设计 临床试验设计
- 生物统计学 生物统计学
背景情况:
- 适应性干预将医疗保健量身定制为个别患者的需要.
- 顺序多重分配随机试验 (SMART) 越来越多地用于开发最佳的适应性干预措施.
- 进行SMART研究带来了独特的后勤和技术挑战,包括管理随机化和数据保密性.
研究的目的:
- 在使用REDCap.Cap.在SMART研究中自动化双随机化的策略.
- 展示REDCap如何促进严格的SMART研究实施.
- 为未来进行SMART的研究人员提供REDCap项目XML文件.
主要方法:
- 在2022年1月至3月期间,与新泽西州成年居民进行了SMART,以优化COVID-19测试吸收率.
- REDCap被用于数据收集和管理该研究的双随机化要求.
- 使用应用程序编程接口 (API) 来自动化双随机化,并与 REDCap 的内置随机化功能结合使用.
主要成果:
- 利用REDCap的随机化功能,自动化了该研究的双随机化过程.
- 使用API成功自动化了SMART设计所需的额外随机化.
- 该研究成功实施了一项适应性干预措施,以增加COVID-19测试采用率.
结论:
- REDCap为纵向数据收集和实施SMART研究提供了强大的工具.
- 通过REDCap自动化双随机化可以显著减少SMART研究中的错误和偏差.
- 这种方法提高了适应性干预研究的可行性和严格性.
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