缺失的数据问题:在比较有效性研究中对缺失的电子健康记录数据的影响的经验评估
Yizhao Zhou1,2, Jiasheng Shi1,2, Ronen Stein2,3
1Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
概括
电子健康记录 (EHR) 中缺少的数据可能会导致比较有效性研究 (CER) 的偏差. 在处理缺失的EHR数据方面,分线平滑归算往往优于多次归算,减少了偏差和功耗损失.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 临床研究 临床研究
背景情况:
- 电子健康记录 (EHR) 中缺少的数据给比较有效性研究 (CER) 带来了挑战.
- 缺失数据的类型和模式可以显著影响研究结果.
- 准确的归算方法对于使用EHR数据的可靠CER至关重要.
研究的目的:
- 使用EHRs量化缺少数据对CER的影响.
- 为了比较多重归算和spline平滑方法的性能,用于处理CER中缺失的数据.
主要方法:
- 进行了一项实证 (模拟) 研究,以评估治疗效果估计中的偏差和功耗损失.
- 使用EHR数据模拟了各种缺失数据场景.
- 倾向性得分被用来控制混,并比较多重归因和脊柱平滑.
主要成果:
- 间隙平滑归算的结果与完整数据相比,当缺失取决于疾病进展和临床实践时,结果与完整数据相比.
- 线条平滑通常优于多重归算,显示较少的偏差和功耗损失.
- 多重归算在特定场景中表现出有效性,缺少的数据不依赖于疾病进展.
结论:
- 在EHR中缺少数据可能会导致偏见的估计和CER中的错误负面,即使在归算后.
- 利用时间疾病轨迹信息对于在CER中归因缺少的EHR数据至关重要.
- 在选择归算方法时,应考虑缺失数据率和效果大小,以获得最佳的CER结果.
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