为什么在使用电子健康记录数据进行纵向分析时应提取推的访问间隔:检查访问机制和对评估的敏感性,而不是随机
Rose H Garrett1,2, Masum Patel1, Brian M Feldman1,3,4
1Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada.
Statistics in medicine
|May 19, 2025
概括
电子健康记录 (EHR) 提供了宝贵的纵向数据. 考虑到医生推的访问间隔,可以改善疾病轨迹分析,并避免不规则的患者评估带来的偏见.
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) 产生了丰富的纵向患者数据.
- 在EHR中不规则的评估时间,通常与患者健康有关,可能会对疾病过程的估计产生偏见.
- 忽视评估时间的信息性质可能会导致不准确的结论.
研究的目的:
- 为了提高疾病轨迹的估计使用医生推的访问间隔从EHRs.
- 描述患者评估过程,并评估对非随机评估的敏感性.
- 提高涉及不规则纵向数据的分析的严格性.
主要方法:
- 利用医生推的间隔作为电子健康记录中的关键信息.
- 描述评估过程和调查非随机评估 (ANAR).
- 将拟议的方法应用于青少年皮肤肌肉炎 (JDM) 队列研究.
主要成果:
- 推的间隔解释了78%的患者评估时间的变化.
- 假设由于疾病恶化的早期访问,估计的疾病轨迹向下移动.
- 结果对随机评估 (AAR) 假设偏差的表现敏感性.
结论:
- 医生推的间隔对于提高纵向数据分析的严谨性至关重要.
- 建议的间隔允许评估AAR假设的可信性和结果敏感性.
- 有不规则纵向数据的研究应将推的访问间隔纳入其分析中.
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