使用SMDI工具包分析现实数据中的缺失模式:应用到链接的EHR-claims药理学流行病学研究中的应用
Sudha R Raman1, Bradley G Hammill2, Pamela A Shaw3
1Department of Population Health Sciences, Duke University School of Medicine, Durham, USA. Sudha.raman@duke.edu.
BMC medical research methodology
|October 19, 2024
概括
在现实世界电子健康记录 (EHR) 中缺少数据是一个挑战. 一个新的工具包 (SMDI) 帮助描述了缺失的数据模式和指导分析,改善了从EHR数据中生成证据.
科学领域:
- 现实世界的数据分析分析.
- 药学流行病学 药学流行病学
- 医疗信息学 医疗信息学
背景情况:
- 在混变量中缺少数据是现实数据 (RWD) 研究的常见挑战,包括电子健康记录 (EHR).
- 描述缺失的数据模式和理解它们的机制对于产生强有力的证据至关重要.
- 本研究应用了一套新的工具包,以在经验案例中解决这些挑战.
研究的目的:
- 应用结构性缺失数据调查 (SMDI) 工具包来描述药物流行病学研究中缺失的数据模式.
- 为了说明基于已识别的缺失机制进行数据分析的决策过程.
- 在现实环境中展示SMDI工具包的实际应用.
主要方法:
- 利用SMDI工具包分析SGLT2i和DPP-4i发起者的心血管结果的研究中缺失的数据.
- 专注于在链接的EHR-Medicare索赔数据集中部分观察到的混因子 (HbA1c,BMI).
- 采用SMDI的描述功能和诊断测试来探索失踪情况并为缓解策略提供信息.
主要成果:
- 观察到HbA1c (63.6%) 和BMI (16.5%) 的高缺失率.
- 诊断测试提供了对失踪模式,共变量分布和可预测性的见解.
- 使用随机森林的链式方程进行多次归算,有效地解决了缺少的混数据,使效应估计与先前的研究保持一致.
结论:
- 成功展示了SMDI工具包在现实世界RWD环境中的实际应用.
- 根据SMDI工具包的见解,选择了适当的分析方法来处理缺失的数据.
- 这种方法提高了透明度,并提高了从RWD获得可靠证据的能力.
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