在OHCA注册表中缺少数据:多种计量方法如何影响研究结论-II论文
Stella Jinran Zhan1, Seyed Ehsan Saffari1, Marcus Eng Hock Ong2
1Centre for Biomedical Data Science, Duke-NUS Medical School, Singapore 169857, Singapore.
Journal of clinical medicine
|January 28, 2026
概括
多重归算 (MI) 方法,特别是基于树的算法,如CART和RF,在医院外心脏骤停 (OHCA) 注册表中提供了对缺失数据的可靠处理. 这些先进的技术与简单的方法相比,提供了更可靠的统计有效性.
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:
- 临床观察研究中缺少的数据,如医院外心脏骤停 (OHCA) 注册表,可能会显著影响统计学有效性.
- 完整案例分析 (CCA) 和单一归算方法是不够的,因为它们不考虑归算不确定性.
- 多重归算 (MI) 是缺失随机 (MAR) 数据的标准,但存在实施挑战.
研究的目的:
- 在对OHCA数据的关联分析中评估MI的性能.
- 将MI方法与CCA和单次归算技术进行比较.
- 为了确定最佳的MI算法来处理OHCA注册表中缺少的数据.
主要方法:
- 一个使用新加坡OHCA注册数据 (N=13,274) 的模拟框架,其中20%,30%和40%是人工引入的MAR数据.
- 使用预测平均匹配 (PMM),随机森林 (RF) 和分类和回归树 (CART) 实现MI,使用5-20个归算.
- 对发出警报和旁观者CPR协会的后勤回归模型中偏差和精度的评估.
主要成果:
- 与PMM相比,CART表现优越,在失踪级别中产生更准确的系数和稳定的置信区间 (CI).
- K-Nearest Neighbors (KNN) 提供了类似的点估计,但低估了归算不确定性.
- PMM表现出更大的偏差和不太稳定的CI,有时表现与CCA相比;MI方法适当地捕捉了比单个归算更广泛的CI的归算不确定性.
结论:
- 在OHCA研究中MI的有效性高度依赖于所选择的归算算法.
- 基于树的方法,特别是CART和RF,在OHCA数据分析中被证明是最强大和最一致的.
- 使用CART/RF的MI建议在OHCA研究中得出可靠的结论,而PMM应谨慎使用.
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