Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets
Statistical comparison of two ROC-curve estimates obtained from partially-paired datasets
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DOI：
10.1177/0272989x9801800118
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发表时间：
1998-01-01
影响因子：
3.6
通讯作者：
Roe, CA
中科院分区：
文献类型：
--
作者：
Metz, CE;Herman, BA;Roe, CA
作者提出了一种新的ROC曲线拟合和统计检验的通用方法，该方法允许研究人员利用在两种诊断模式的实验比较中收集的所有数据，即使某些患者没有同时使用这两种模式进行研究。他们的新算法Rockit将以前的算法作为特例包含在内。它执行以前ROC软件提供的所有分析，ACID为所有估计提供95%的可信区间。Rockit在50多万个不同大小和配置的计算机模拟数据集上进行了测试，这些数据集代表了一系列人口ROC曲线。该算法对99.8%以上的数据集都有较好的收敛效果。对于通常在实践中遇到的数据集，新算法对差值的统计检验的第I类错误率非常好，但对于一些极端情况下出现的数据集，估计值偏离了阿尔法。
The authors propose a new generalized method for ROC-curve fitting and statistical testing that allows researchers to utilize all of the data collected in an experimental comparison of two diagnostic modalities, even if some patients have not been studied with both modalities. Their new algorithm, ROCKIT, subsumes previous algorithms as special cases. It conducts all analyses available from previous ROC software acid provides 95% confidence intervals for all estimates. ROCKIT was tested on more than half a million computer-simulated datasets of various sizes and configurations representing a range of population ROC curves. The algorithm successfully converged for more than 99.8% of all datasets studied. The type I error rates of the new algorithm's statistical test for differences in A, estimates were excellent for datasets typically encountered in practice, but diverged from alpha for datasets arising from some extreme situations.