title: Semi-parametric maximum likelihood estimates for ROC curves of continuous-scale tests.
abstract: In this paper, we propose a new semi-parametric maximum likelihood (ML) estimate of a receiver operating characteristic (ROC) curve that satisfies the property of invariance of the ROC curve and is easy to compute. We show that our new estimator is sqrt[n]-consistent and has an asymptotically normal distribution. Our extensive simulation studies show that the proposed method is efficient and robust. Finally, we illustrate the application of the proposed estimator in a real data set.
authors.affiliation: HSR&D Center of Excellence, VA Puget Sound Health Care System, Seattle, WA 98108, USA. azhou@u.washington.edu
authors.forename: Xiao-Hua
authors.initials: XH
authors.lastname: Zhou
authors.forename: Huazhen
authors.initials: H
authors.lastname: Lin
mesh_terms: D015295:Antigens, Tumor-Associated, Carbohydrate / Q000032:analysis; D015415:Biomarkers; D002256:Carbonic Anhydrases / Q000032:analysis; D003955:Diagnostic Tests, Routine / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D010190:Pancreatic Neoplasms / Q000175:diagnosis; D012372:ROC Curve
chemical_list: D015295:Antigens, Tumor-Associated, Carbohydrate; D015415:Biomarkers; C483903:CA13 protein, human; D002256:Carbonic Anhydrases