分析验证新型数字临床措施的方法:使用现实世界的数据集进行实施可行性评估
Simon Turner1, Lysbeth Floden2, Leif Simmatis3
1Digital Medicine Society, Boston, MA, United States.
Journal of medical Internet research
|November 17, 2025
概括
基于传感器的数字健康技术 (sDHTs) 为药物开发产生数字措施 (DMs). 确认因素分析 (CFA) 有效地验证了新型DM与参考措施的对比,指导研究设计以获得可靠的结果.
科学领域:
- 生物统计学 生物统计学
- 数字卫生技术数字卫生技术
- 药物开发 药物开发
背景情况:
- 基于传感器的数字健康技术 (sDHT) 产生了对科学和临床决策至关重要的数字措施 (DM).
- DMs可以加速药物开发,降低试验成本,改善护理准入.
- 新型DM的分析验证 (AV) 是一个挑战,因为缺乏既定的参考措施 (RMs).
研究的目的:
- 评估使用现实数据实施分析验证 (AV) 统计方法的可行性.
- 检查研究设计因素如何影响DM和RM之间的关系的估计.
- 为新的DM提供关于AV标准化的指导.
主要方法:
- 利用了四个现实世界的数据集 (城市贫困,STAGES,mPower, Brighten) 来模拟AV研究.
- 评估研究设计属性:时间连贯性,构造连贯性和数据完整性.
- 进行比较的统计方法:皮尔森相关系数 (PCC),简单线性回归 (SLR),多重线性回归 (MLR) 和确认因子分析 (CFA).
主要成果:
- 确认因素分析 (CFA) 模型通常显示可接受的适合性和估计因素相关性.
- 由CFA衍生的因子相关性始终比Pearson相关系数 (PCC) 强.
- 在具有高时间和结构一致性的假设研究中观察到最强的相关性.
结论:
- 评估的统计方法对于DMs的AV中的真实世界数据是可行的.
- 建议进行确认因子分析 (CFA) 来评估新的DM-RM关系.
- 结果为AV研究设计提供了实际建议,促进SDHTs的标准化验证.
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