title: Transformation-invariant and nonparametric monotone smooth estimation of ROC curves.
abstract: 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.
authors.affiliation: Department of Statistics, Virginia Tech, Blacksburg, VA 24061, USA.
authors.forename: Pang
authors.initials: P
authors.lastname: Du
authors.forename: Liansheng
authors.initials: L
authors.lastname: Tang
mesh_terms: D015982:Bias; D003955:Diagnostic Tests, Routine / Q000592:standards / Q000706:statistics & numerical data; D036262:Empirical Research; D005189:False Positive Reactions; D012372:ROC Curve; D018709:Statistics, Nonparametric