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通过试验完整性推进翻译科学:基于REDCap的方法来缓解欺诈和偏见
Gaylen E Fronk1, Larry W Hawk2, Andrew Cates3
1Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA.
Journal of clinical and translational science
|January 7, 2026
概括
分散临床试验 (DCT) 可以改善研究,但面临数据完整性的挑战. 新的REDCap模块Cheatblocker和QuotaConfig有效打击欺诈和抽样偏差,提高了DCT数据的可靠性.
科学领域:
- 临床试验方法论 临床试验方法论
- 研究中的数据管理研究中的数据管理
- 翻译科学 翻译科学
背景情况:
- 分散临床试验 (DCT) 在招聘速度,覆盖范围和样本代表性方面比传统试验具有优势.
- 仍然存在对DCT数据完整性的担忧,特别是对欺诈和抽样偏差的敏感性.
- 确保强大的数据完整性对于DCT的有效性和公共卫生影响至关重要.
研究的目的:
- 介绍和描述两个新的工具,Cheatblocker和QuotaConfig,这些工具是为了解决DCT中的数据完整性问题而开发的.
- 证明这些工具在打击欺诈和抽样偏见方面的成功实施.
- 讨论这些工具在翻译科学中的DCT中增强数据严格性的更广泛含义.
主要方法:
- 开发和实施REDCap的外部模块Cheatblocker,以检测和防止欺诈性数据输入.
- 开发和实施REDCap的外部模块QuotaConfig,以通过受控招聘减轻采样偏差.
- 案例研究示例说明了两个模块在现实世界DCT场景中的成功应用.
主要成果:
- 欺诈拦截器成功识别并阻止了在DCT中欺诈性数据输入.
- QuotaConfig有效地管理了参与者招聘,减少了抽样偏差.
- 这两个模块都成功地集成到REDCap系统中,证明了其实用性.
结论:
- 开发的REDCap模块Cheatblocker和QuotaConfig提供了有效的解决方案,用于提高分散的临床试验中的数据完整性.
- 这些工具有助于提高研究严谨性和效率,这对于最大限度地提高公共卫生效益至关重要.
- 成功实施突出了技术解决方案的潜力,以解决DCT数据质量持续存在的挑战.
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