用经验概率来计算AUC和pAUC的信心区间
Yumin Zhao1, Xue Ding2, Mai Zhou2
1Eli Lilly and Company, Indianapolis, Indiana, USA.
Statistics in medicine
|July 21, 2025
概括
我们介绍了一种新的经验概率方法,用于评估医学诊断测试. 这种方法为接收器运行特征曲线 (AUC) 和部分AUC (pAUC) 下的区域提供了准确的置信区间和假设测试.
科学领域:
- 生物统计学 生物统计学
- 医学诊断测试评价 医学诊断测试评价
背景情况:
- 接收器运行特征曲线下的面积 (AUC) 和部分AUC (pAUC) 是医疗诊断测试性能的关键指标.
- 现有的AUC和pAUC估计方法通常涉及复杂的方差计算.
研究的目的:
- 提出一种新的两样本实证概率方法用于假设测试和AUC和pAUC的置信区间构建.
- 为现有方法提供一个统计学上稳健和计算上更简单的替代方案.
主要方法:
- 为非参数设置开发了一种两样实证概率比测试.
- 测试统计数据在零假设下异常遵循奇平方分布.
- 这种方法避免了需要估计复杂的比例因子或差异.
主要成果:
- 模拟表明,在各种场景中,与竞争对手相比,拟议的方法的性能优越.
- 经验概率比测试提供了准确的千平方分布,简化了统计推理.
- 提供了现实数据示例和附带的R代码,以说明实际应用.
结论:
- 拟议的经验概率方法为AUC和pAUC的统计推断提供了一种竞争性和高效的方法.
- 这种方法简化了评估医疗诊断测试的过程,提高了其可靠的应用.
- 该研究为医学诊断领域的研究人员和从业人员提供了宝贵的工具.
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