Coffin M, Sukhatme S
Department of Mathematical Sciences, Clemson University, South Carolina 29634-1907, USA.
Biometrics. 1997 Sep;53(3):823-37.
A receiver operating characteristic (ROC) curve expresses the probability of a true positive (PTP) as a function of the probability of a false positive (PFP) for all possible values of the cutoff between cases and controls. Theta, the area under ROC curve, is a measure of the diagnostic ability of the separator variable. The usual nonparametric estimate of theta is shown to be based when the separator is measured with error. An expression for the largest-order term of the bias is found. The observed values and the measurement error variance are used to form a kernel estimate of the underlying distribution. These kernel estimates are used to estimate the bias. Monte Carlo simulation indicates that, for several families of distributions, the bias-corrected estimators have smaller bias and comparable MSE to the usual estimator. An application to the data of Clayton, Moncrieff, and Roberts (1967, British Medical Journal 3, 133-136) illustrates the technique.
受试者工作特征(ROC)曲线表示真阳性概率(PTP)与假阳性概率(PFP)之间的函数关系,其中假阳性概率是针对病例和对照之间所有可能的截断值。ROC曲线下面积θ是分离变量诊断能力的一种度量。当分离变量存在测量误差时,通常的θ非参数估计是有偏的。我们找到了偏差最大阶项的表达式。利用观测值和测量误差方差形成潜在分布的核估计。这些核估计用于估计偏差。蒙特卡罗模拟表明,对于几个分布族,偏差校正估计量的偏差较小,且均方误差与通常的估计量相当。对克莱顿、蒙克里夫和罗伯茨(1967年,《英国医学杂志》3,133 - 136)的数据应用说明了该技术。