一个新的估计器,用于双向部分AUC
Elias Chaibub Neto1, Vijay Yadav2, Solveig K Sieberts2
1Sage Bionetworks, 2901 Third Avenue, 98121, Seattle, USA. elias.chaibub.neto@sagebase.org.
BMC medical informatics and decision making
|February 20, 2024
概括
一种新方法有效地估计了用于诊断测试的ROC曲线下的双向部分面积 (AUC). 这种更快的计算允许对大型数据集进行比较,克服了以前方法的局限性.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 医学诊断 医学诊断 医学诊断
背景情况:
- 双向部分AUC量化了限制敏感度和特异性范围内的分类器性能.
- 现有的tpAUC估计器对于大型数据集来说是计算密集的,这阻碍了引导式比较.
- 非参数修剪的曼-惠特尼U统计学具有很高的计算复杂性 (O(n^2).
研究的目的:
- 开发一种新的,计算效率高的估计器,用于双向部分AUC.
- 能够对大数据集上的诊断测试和分类器进行可靠的比较.
- 减少依赖引导的统计比较的计算负担.
主要方法:
- 导出了一个利用图形和概率 AUC 表示的新估计器.
- 使用 pROC R 包实现,采用梯形规则方法.
- 计算复杂度降低到O (n).
主要成果:
- 与原来的tpAUC方法相比,拟议的估计器表明偏差减少.
- 由于O (n) 复杂度,大样本大小的计算时间显著加快.
- 经验评估通过模拟和现实世界数据集证实了性能.
结论:
- 新型估计器提高了双向部分AUC计算效率.
- 在大规模研究中促进诊断测试和分类器的比较分析.
- 克服了以前用于基于引导式比较的方法的计算瓶.
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