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用于接收器操作特性离散评分数据的多读者方法的蒙特卡罗验证:析因实验设计

Monte Carlo validation of a multireader method for receiver operating characteristic discrete rating data: factorial experimental design.

作者信息

Dorfman D D, Berbaum K S, Lenth R V, Chen Y F, Donaghy B A

机构信息

Department of Radiology, University of Iowa, Iowa City, USA.

出版信息

Acad Radiol. 1998 Sep;5(9):591-602. doi: 10.1016/s1076-6332(98)80294-8.

DOI:10.1016/s1076-6332(98)80294-8
PMID:9750888
Abstract

RATIONALE AND OBJECTIVES

The authors conducted a series of null-case Monte Carlo simulations to evaluate the Dorfman-Berbaum-Metz (DBM) method for comparing modalities with multireader receiver operating characteristic (ROC) discrete rating data.

MATERIALS AND METHODS

Monte Carlo simulations were performed by using discrete ratings on fully crossed factorial designs with two modalities and three, five, and 10 hypothetical readers. The null hypothesis was true for all simulations. The population ROC areas, latent variable structures, case sample sizes, and normal/abnormal case sample ratios used in another study were used in these simulations.

RESULTS

For equal allocation ratios and small (Az = 0.702) and moderate (Az = 0.855) ROC areas, the empirical type I error rate closely matched the nominal alpha level. For very large ROC areas (Az = 0.961), however, the empirical type I error rate was somewhat smaller than the nominal alpha level. This conservatism increased with decreasing case sample size and asymmetric normal/abnormal case allocation ratio. The empirical type I error rate was sometimes slightly larger than the nominal alpha level with many cases and few readers, where there was large residual, relatively small treatment-by-case interaction and relatively large treatment-by-reader interaction.

CONCLUSION

The results suggest that the DBM method provides trustworthy alpha levels with discrete ratings when the ROC area is not too large and case and reader sample sizes are not too small. In other situations, the test tends to be somewhat conservative or slightly liberal.

摘要

原理与目的

作者进行了一系列零病例蒙特卡罗模拟,以评估用于比较多读者接收器操作特征(ROC)离散评分数据的不同模式的多尔夫曼 - 贝鲍姆 - 梅茨(DBM)方法。

材料与方法

通过在具有两种模式以及三个、五个和十个假设读者的完全交叉析因设计上使用离散评分来进行蒙特卡罗模拟。所有模拟的原假设均为真。这些模拟使用了另一项研究中所采用的总体ROC面积、潜在变量结构、病例样本量以及正常/异常病例样本比例。

结果

对于相等的分配比例以及较小(Az = 0.702)和中等(Az = 0.855)的ROC面积,经验性I型错误率与名义α水平紧密匹配。然而,对于非常大的ROC面积(Az = 0.961),经验性I型错误率略小于名义α水平。这种保守性随着病例样本量的减少以及正常/异常病例分配比例的不对称而增加。在病例多而读者少的情况下,经验性I型错误率有时会略大于名义α水平,此时存在较大残差、相对较小的病例与处理交互作用以及相对较大的读者与处理交互作用。

结论

结果表明,当ROC面积不太大且病例和读者样本量不太小的时候,DBM方法在离散评分情况下能提供可靠的α水平。在其他情况下,该检验往往会略显保守或略微宽松。

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