对共变量特异的ROC曲线分析可以在评估诊断准确性时考虑共变量子组之间的差异
Jenny Lee1, Nick van Es2, Toshihiko Takada3
1Epidemiology and Data Science, Amsterdam UMC location University of Amsterdam, Amsterdam, The Netherlands.
Journal of clinical epidemiology
|June 9, 2023
概括
在接受器操作特征 (ROC) 曲线分析中考虑患者共变量,用于D-二次元测试,显示出性能差异. 条件ROC曲线改善了肺栓塞排除的门选择.
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
- 临床流行病学临床流行病学
背景情况:
- 接收器操作特征 (ROC) 曲线分析是评估诊断测试性能的一种标准工具.
- 当患者特征 (共变量) 影响结果时,传统的ROC分析可能无法完全代表测试性能.
- D-二聚体检测经常用于排除肺栓塞,但其性能可能因患者子组而异.
研究的目的:
- 为了说明在ROC曲线分析中计算共变量的方法.
- 检查临床共变量如何影响D-二次体测试的性能和阳性值,以排除肺栓塞.
- 为了将共同变量特定的ROC曲线与标准ROC曲线进行比较.
主要方法:
- 贝叶斯的非参数共变特异性ROC曲线是使用个体患者数据构建的.
- 标准ROC曲线也被生成用于比较.
- 定义了三种情景,以分类共变量分布和测试性能之间的关系:相同/相同,不同/相同和不同/不同.
- 开发了与共变量调整的ROC曲线.
主要成果:
- 在不同年龄组中观察到D-二元度和测试性能的显著差异 (情景3).
- 在某些共同变量子组中,对于相同的敏感性需要不同的阳性度值.
- D-二次体显示了类似的性能,但对于YEARS算法项目 (场景2) 和性别 (场景1) 的分布不同.
- 与共变量调整的模型给出了与标准ROC分析相比的曲线下面积 (AUC) 值.
结论:
- 传统的ROC曲线可能会在存在显著的子组差异的情况下,提供对测试性能的一致表示.
- 估计包含共变量的条件ROC曲线可以提高选择临床适用的阳性值的能力.
- 与共变量调整的分析改善了在不同患者群体中对诊断测试表现的细微解释.
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