计算和缺失指标处理缺失的纵向数据:基于电子健康记录数据的数据模拟分析
Molly Ehrig1, Garrett S Bullock1, Xiaoyan Iris Leng1
1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Medical Center Blvd, Winston Salem, NC, 27157, United States, 1 3367133469.
JMIR medical informatics
|March 13, 2025
概括
缺失的指标方法不会改善或降低模型性能或纵向数据分析中的归算准确性. 这种方法在处理用于预测模型的电子健康记录中缺少的数据时,既没有好处也没有不利.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 电子健康记录 (EHR) 中缺少的数据带来了分析挑战,包括偏见和减少的统计能力.
- 缺失指标方法,将缺失作为一个类别来处理,是一种简单的方法来处理未知的共同变量值.
- 与横截面分析相比,其在纵向数据中的实用性,即可以利用重复测量,仍然不清楚.
研究的目的:
- 评估缺失指标方法对模型性能和纵向数据的归算精度的影响.
- 在模拟研究中评估其有效性,模仿基于EHR的临床预测模型,对老年人中跌倒的情况进行评估.
主要方法:
- 使用混合效应物流回归模拟纵向二进制结果.
- 嵌入了时间不变和动态预测器,在随机和非随机场景下诱导缺失的数据.
- 通过使用接收器操作特征曲线 (AUROC) 下的面积和归算质量通过规范化根-平方平均误差和错误分类的百分比来评估模型性能.
主要成果:
- 模型性能 (AUROC) 和归算准确性与缺失指标或没有缺失指标相似,无论缺失数据机制 (随机或非随机).
- 包括缺失指标并没有影响性能或归算质量,即使结果与缺失有关.
- 推算方法显示了可比的AUROC方法来完成病例分析,尽管完整的病例分析具有更大的可变性.
结论:
- 缺失的指标方法既不能提高,也不能降低纵向数据建模中的性能或归算精度.
- 需要进一步的研究来探索其在使用纵向数据进行预测建模中的实用性,特别是在高维设置中.
关键词:
欧洲人权理事会 欧洲人权理事会临床预测模型的临床预测模型.电子健康记录数据 电子健康记录数据电子健康记录是电子健康记录.布布布的情况是:归算是指指责一个人.逻辑回归的逻辑回归纵向数据 纵向数据 纵向数据缺失的数据 缺失的数据缺失的指标方法缺失的指标方法年龄较大的成年人.预测模型 预测模型预测建模预测建模模拟研究是一种模拟研究.更多相关视频
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