在基于电子健康记录的比较有效性研究中,对信息性缺失的数据进行双采样
Alexander W Levis1, Rajarshi Mukherjee2, Rui Wang2,3
1Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Statistics in medicine
|December 5, 2024
概括
双采样提供了一个强大的解决方案,用于处理电子健康记录 (EHR) 中缺失的数据,这些数据是不随机缺失的 (MNAR). 这种方法使得可靠的估计和推断的因果关系的影响,即使有复杂的数据问题.
科学领域:
- 生物统计学 生物统计学
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 电子健康记录 (EHR) 中缺少数据是常见的,特别是当数据缺失时不是随机的 (MNAR).
- 目前对MNAR数据的敏感性分析往往缺乏可操作的结论.
- 腹腔外科手术结果研究经常遇到MNAR数据.
研究的目的:
- 引入和评估双重抽样作为一种方法来处理在EHR中的MNAR结果数据.
- 为了能够准确地估计和推断因果关系,尽管缺少数据.
- 为健康结果研究提供强大的统计工具.
主要方法:
- 在双重抽样下,开发了用于识别联合分布的假设.
- 获得了平均因果治疗效果 (ACTE) 的高效和可靠估计器.
- 通过模拟,通过非参数和随机缺失 (MAR) 模型进行比较估计.
主要成果:
- 双样采样为使用MNAR数据进行因果推理提供了一个框架.
- 建议的估计器证明了效率和稳定性.
- 该方法扩展到处理任意数据粗化机制.
结论:
- 双样采样是一种可行的策略,可以在EHR研究中减轻MNAR数据.
- 由此产生的估计器提供了更好的因果效应估计.
- 这种方法提高了不完整数据的健康研究结果的可靠性.
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