Chakraborty Dev
Department of Radiology, University of Pennsylvania, Science Center, Philadelphia 19104, USA.
Acad Radiol. 2002 Feb;9(2):147-56. doi: 10.1016/s1076-6332(03)80164-2.
Statistical power, defined as the probability of detecting real differences between imaging modalities, determines the cost in terms of readers and cases of conducting receiver operating characteristic (ROC) studies. Neglect of location information in lesion-detection studies analyzed with the ROC method can compromise power. Use of the alternative free-response ROC (AFROC) method, which considers location information, has been discouraged, because it neglects intraimage correlations. The relative statistical powers of the two methods, however, have not been tested. The purpose of this study was to compare the statistical power of ROC and AFROC methods using simulations.
A new model including intraimage correlations was developed to describe the decision variable sampling and to simulate data for ROC and AFROC analyses. Five readers and 200 cases (half of which contained one signal) were simulated for each trial. Two hundred trials, equally split between the null hypothesis and alternative hypothesis, were run. Ratings were analyzed with the Dorfman-Berbaum-Metz method, and separation of the null hypothesis and alternative hypothesis distributions was calculated.
The AFROC method yielded higher power than the ROC method. Separation of the null hypothesis and alternative hypothesis distributions was larger by a factor of 1.6 regardless of the presence or absence of intraimage correlations. The effect of the incorrect localizations during ROC analysis of localization data is believed to be the major reason for the enhanced power of the AFROC method.
The AFROC method can yield higher power than the ROC method for studies involving lesion localization. Greater consideration of this methodology is warranted.
统计效能定义为检测成像模式之间真实差异的概率,它决定了进行接受者操作特征(ROC)研究所需的读者和病例数量成本。在使用ROC方法分析的病变检测研究中,忽略位置信息会影响效能。使用考虑位置信息的替代自由响应ROC(AFROC)方法一直不被提倡,因为它忽略了图像内相关性。然而,这两种方法的相对统计效能尚未经过测试。本研究的目的是通过模拟比较ROC和AFROC方法的统计效能。
开发了一个包含图像内相关性的新模型,以描述决策变量抽样并模拟用于ROC和AFROC分析的数据。每次试验模拟5名读者和200个病例(其中一半包含一个信号)。进行了200次试验,在原假设和备择假设之间平均分配。使用Dorfman-Berbaum-Metz方法分析评分,并计算原假设和备择假设分布的分离度。
AFROC方法的效能高于ROC方法。无论是否存在图像内相关性,原假设和备择假设分布的分离度都高出1.6倍。在对定位数据进行ROC分析时,错误定位的影响被认为是AFROC方法效能提高的主要原因。
对于涉及病变定位的研究,AFROC方法的效能可能高于ROC方法。有必要更多地考虑这种方法。