比较两种相关受者工作特性曲线的置换检验

Okeh Uchechukwu Marius, Onyeagu I. Sidney
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引用次数: 0

摘要

受试者工作特征(ROC)曲线下的面积(AUC)是比较两条ROC曲线时的汇总测量。然而,当两条ROC曲线交叉且具有相同的auc时,这种汇总测量的信息量较小。为了检测ROC曲线之间的差异,能够解决这一问题的可交换性标签内两个诊断测试,另一个排列测试基于主客体之间排列的标签对象在每一个诊断测试提出了评估的改变auc一双连续匹配的数据来自两个诊断测试程序同时拥有十几和病变在每个测试的主题。在auc相等的零假设下,将Wilcoxon符号秩检验统计量修改为置换检验。开发了一种算法,用于执行成对检验结果的所有不同排列的完整枚举,该算法提供了精确的p值。使用模拟数据,本文提出的检验在统计能力上与Braun和Alonzo提出的修正符号检验相比较,但本文提出的检验具有更好的操作特性,即检测交叉替代的统计能力更强,检验规模的保守性更低,在平均至少0.8个auc的参数范围内,相关性至少为0.4,样本量小到中等。同样,在应用实际数据时,所提出的检验更有可能拒绝名义水平为0.05的AUC1和AUC2相等的原假设,所提出的检验的p值为0.0312,而Braun和Alonzo的检验的p值为0.0387。这是因为所提议的检验经过修改以调整值中存在的零差异,并考虑值的符号以及值的绝对秩。两项诊断试验AUC1和AUC2的估计值分别为0.668和0.887,表明在特异性大于0.7的情况下,AUC2即2小时100g口服葡萄糖耐量试验(OGTT)优于AUC1(2小时70g OGTT)。
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A Permutation Test for Comparing Two Correlated Receiver Operating Characteristic Curves
The area under the Receiver Operating Characteristic (ROC) curve (AUC) is a summary measure when comparing two ROC curves. However, this summary measure is less informative when two ROC curves cross and have the same AUCs. In order to detect differences between ROC curves and to be able to tackle the problem of exchangeability of the labels between two diagnostic tests within subject, an alternative permutation test based on between-subject permutations of the labels of the subjects within each diagnostic test is proposed for assessing a change in the AUCs in a continuous matched pair of data from two diagnostic test procedures having both non-diseased and diseased subject in each of the test. The Wilcoxon signed rank test statistic was modified as a permutation test under the null hypothesis of equality of AUCs. An algorithm for carrying out complete enumeration of all the distinct permutations of the paired test results was developed which provides exact p-values. Using simulated data, the proposed test compares in statistical power to the modified sign test proposed by Braun and Alonzo but the proposed test has better operating characteristics, that is greater statistical power to detect a crossing alternative and is less conservative in test size and in the range of parameters of at least 0.8 of AUCs on the average with a correlation of at least 0.4 and small to moderately large sample sizes. Similarly in applying real life data, the proposed test has the more likelihood of rejecting null hypothesis of equality of AUC1 and AUC2 at nominal level of 0.05 with the proposed test having a p-value of 0.0312 against the Braun and Alonzo’s test with a p-value of 0.0387. This is because the proposed test is modified to adjust for the presence of zero differences in values and considers the signs of values as well as the absolute ranks of values. Also the estimates of AUC1 and AUC2 for the two diagnostic tests are 0.668 and 0.887 respectively showing that AUC2, that is 2hour 100g Oral Glucose Tolerance Test (OGTT) is superior to AUC1 (2hour 70g OGTT) at a time that the specificity is greater than 0.7.
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