Du Pang, Tang Liansheng
Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
Stat Med. 2009 Jan 30;28(2):349-59. doi: 10.1002/sim.3465.
When a new diagnostic test is developed, it is of interest to evaluate its accuracy in distinguishing diseased subjects from non-diseased subjects. The accuracy of the test is often evaluated by receiver operating characteristic (ROC) curves. Smooth ROC estimates are often preferable for continuous test results when the underlying ROC curves are in fact continuous. Nonparametric and parametric methods have been proposed by various authors to obtain smooth ROC curve estimates. However, there are certain drawbacks with the existing methods. Parametric methods need specific model assumptions. Nonparametric methods do not always satisfy the inherent properties of the ROC curves, such as monotonicity and transformation invariance. In this paper we propose a monotone spline approach to obtain smooth monotone ROC curves. Our method ensures important inherent properties of the underlying ROC curves, which include monotonicity, transformation invariance, and boundary constraints. We compare the finite sample performance of the newly proposed ROC method with other ROC smoothing methods in large-scale simulation studies. We illustrate our method through a real life example.
当开发一种新的诊断测试时，评估其区分患病个体与未患病个体的准确性是很有意义的。测试的准确性通常通过接收者操作特征（ROC）曲线来评估。当潜在的ROC曲线实际上是连续的时候，对于连续的测试结果，平滑的ROC估计通常更可取。不同的作者已经提出了非参数和参数方法来获得平滑的ROC曲线估计。然而，现有方法存在某些缺点。参数方法需要特定的模型假设。非参数方法并不总是满足ROC曲线的固有属性，如单调性和变换不变性。在本文中，我们提出了一种单调样条方法来获得平滑的单调ROC曲线。我们的方法确保了潜在ROC曲线的重要固有属性，包括单调性、变换不变性和边界约束。在大规模模拟研究中，我们将新提出的ROC方法的有限样本性能与其他ROC平滑方法进行了比较。我们通过一个实际例子来说明我们的方法。
