Bayesian bootstrap estimation of ROC curve
Bayesian bootstrap estimation of ROC curve
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DOI：
10.1002/sim.3366
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发表时间：
2008-11-20
影响因子：
2
通讯作者：
Roy, Anindya
中科院分区：
文献类型：
--
作者：
Gu, Jiezhun;Ghosal, Subhashis;Roy, Anindya
受试者工作特征曲线（ROC）被广泛应用于诊断或预后试验的鉴别能力。这使得ROC分析成为医学统计学中最活跃的研究领域之一。许多参数和半参数估计方法已被提出来估计ROC曲线及其泛函。在本文中，我们提出了贝叶斯自助法（BB），一个完全的非参数估计方法，ROC曲线及其泛函，如曲线下面积（AUC）。BB方法提供了一个无带宽平滑的经验估计方法，并给出了可信的界限。在模拟研究中的ROC曲线的估计的准确性检查的综合绝对误差。与现有的曲线估计方法相比，BB方法在准确性、鲁棒性和简单性方面表现良好。我们还提出了一个程序的基础上的BB方法来测试的副正态性假设。版权所有（C）2008约翰威利父子有限公司
Receiver operating characteristic (ROC) Curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this paper, we propose the Bayesian bootstrap (BB), a fully nonparametric estimation method, for the ROC curve and its functionals, such as the area under the curve (AUC). The BB method offers a bandwidth-free smoothing approach to the empirical estimate, and gives credible bounds. The accuracy of the estimate of the ROC curve in the simulation studies is examined by the integrated absolute error. In comparison with other existing curve estimation methods, the BB method performs well in terms of accuracy, robustness and simplicity. We also propose a procedure based on the BB approach to test the binormality assumption. Copyright (C) 2008 John Wiley & Sons, Ltd.