估计接收器运行特征曲线,当案例和控制需要不同的转换正常性
Xiaoyu Cai1, Wei Zhang2, Huiyun Li3
1Department of Statistics, George Washington University, Washington, DC, USA.
Statistical methods in medical research
|July 9, 2025
概括
接收器操作特征 (ROC) 曲线分析对于诊断生物标志物至关重要. 现有的方法在患病和非患病组的数据转换不同时失败,导致结果偏差. 一种新的方法提供了准确的ROC曲线估计.
科学领域:
- 生物统计学 生物统计学
- 医学诊断 医学诊断 医学诊断
- 生物标记分析 生物标记分析
背景情况:
- 接收器操作特征 (ROC) 曲线对于评估诊断生物标志物的准确性至关重要.
- 标准方法通常将常见的Box-Cox转换应用于病例和对照中的生物标记数据.
- 来自患病和非患病组的生物标志物数据可能需要不同的转换以达到正常近似值.
研究的目的:
- 为了解决当前ROC曲线估计方法的局限性,当生物标志物数据需要异质转换时.
- 提出一种新的方法来估计 ROC 曲线及其曲线下的面积 (AUC) 以不同的转换.
- 根据现有的参数和非参数方法来评估拟议方法的性能.
主要方法:
- 在异质Box-Cox转换下开发了用于ROC曲线和AUC估计的新统计方法.
- 将拟议的方法与非参数估计器和使用常见Box-Cox转换的估计器进行比较.
- 利用来自国家健康和营养检查调查 (NHANES) 的HIV感染数据进行实证验证.
主要成果:
- 确定使用常见Box-Cox转换的现有方法在数据需要不同的转换时具有偏差.
- 证明拟议的方法提供了ROC曲线和AUC的有效和不太偏的估计.
- 与常见的转换和非参数方法相比,新方法显示了更好的性能.
结论:
- 生物标记数据中的异质转换需要专门的方法来进行准确的ROC分析.
- 拟议的方法有效地处理不同的数据转换,改善诊断生物标志物评估.
- 这种方法提高了ROC曲线和AUC估计在生物统计和临床研究的可靠性.
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