Jackknife variance of the partial area under the empirical receiver operating characteristic curve.
Jackknife variance of the partial area under the empirical receiver operating characteristic curve.
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DOI：
10.1177/0962280214551190
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发表时间：
2017-04
影响因子：
2.3
通讯作者：
Gur D
中科院分区：
文献类型：
--
作者：
Bandos AI;Guo B;Gur D
受试者操作特征（ROC）分析提供了用于评估传统（例如，成像技术和临床实践）和新的（例如，基因组研究、生物标记物开发）诊断问题。ROC曲线的临床/实际相关部分下的面积（部分面积或pAUC）是总结与实际临床实践相关的多个操作点（决策阈值）的诊断准确性的重要性能指标。pAUC的稳健估计值由经验ROC曲线相应部分下的面积提供。我们推导出了经验ROC曲线下部分区域的刀切方差的封闭形式表达式。使用推导出的解析表达式，我们调查的刀切方差和传统的方差估计之间的差异。在有限样本的相关性质证明了在模拟研究。所开发的公式能够以简单的方式估计经验pAUC的方差，从而大大减少计算负担，并提供对变异性结构的重要见解。我们证明，与传统的方法相比，刀切方差具有可忽略的偏差，并导致更合适的Wald型检验的I型错误率。在诊断成像研究的数据集的分析中说明了刀切方差的使用。
Receiver Operating Characteristic (ROC) analysis provides an important methodology for assessing traditional (e.g., imaging technologies and clinical practices) and new (e.g., genomic studies, biomarker development) diagnostic problems. The area under the clinically/practically relevant part of the ROC curve (partial area or pAUC) is an important performance index summarizing diagnostic accuracy at multiple operating points (decision thresholds) that are relevant to actual clinical practice. A robust estimate of the pAUC is provided by the area under the corresponding part of the empirical ROC curve. We derive a closed-form expression for the jackknife variance of the partial area under the empirical ROC curve. Using the derived analytical expression we investigate the differences between the jackknife variances and a conventional variance estimator. The relative properties in finite samples are demonstrated in a simulation study. The developed formula enables an easy way to estimate the variance of the empirical pAUC, thereby substantially reducing the computation burden, and provides important insight into the structure of the variability. We demonstrate that when compared with the conventional approach, the jackknife variance has negligible bias, and leads to a more appropriate type I error rate of the Wald-type test. The use of the jackknife variance is illustrated in the analysis of a dataset from a diagnostic imaging study.
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