在REDCap中通过数据输入触发器自动化共变量适应随机化的工作流
Jacob M Schauer1, Marc O Broxton2, Luke V Rasmussen3
1Division of Biostatistics and Informatics, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, United States.
JAMIA open
|October 3, 2025
概括
集成到REDCap中的共变适应随机化算法 (CARAs) 减少了临床试验中的不平衡. 这个软件管道提高了随机化效率和可复制性,为研究人员.
科学领域:
- 临床试验方法论 临床试验方法论
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 共变量适应性随机化算法 (CARAs) 在随机对照试验 (RCT) 中有效地减少共变量失衡.
- 卡拉在REDCap等电子数据捕获系统中的有限整合阻碍了它们的广泛采用.
- all2GETHER研究是一项专注于艾滋病毒预防的RCT,旨在解决这种整合差距.
研究的目的:
- 开发和实施一个软件管道,以便无地将CARAs集成到REDCap.
- 在REDCap环境中自动化随机化过程.
- 促进在临床研究中使用先进的随机化技术.
主要方法:
- 使用PHP和R开发了一个软件管道,利用REDCap的数据输入触发功能.
- 在保存特定的REDCap表单时,自动随机化被触发.
- 研究人员收到随机分配的自动通知.
主要成果:
- 实施的管道成功地减少了all2GETHER研究中的共同变量失衡.
- 研究组间观察到的差异很小 (连续变量的科恩d=0.003,分类/二进制变量的风险差异<2.4%).
- 整合需要研究人员尽可能少的额外努力.
结论:
- 开发的软件管道有效地减少了使用REDCap.Cap进行的RCT中的共变异不平衡.
- 管道是可重复的,为其他研究提供了实际的解决方案.
- 这种整合提高了临床试验中治疗臂的效率和可比性.
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