title: Comparison of diagnostic markers with repeated measurements: a non-parametric ROC curve approach.
abstract: In this paper we study a class of non-parametric statistics for comparing diagnostic markers with repeated measurements. Using adapted definitions of specificity and sensitivity, we suggest methods to compare the average of sensitivities across all specificities or a range of specificities. The theory allows for correlations introduced by the fact that markers may be obtained from the same patient at multiple visits and that both markers being compared may be obtained from the same patient. Results of the Monte Carlo simulations and an example from a breast cancer setting are provided.
authors.affiliation: Bayer Pharmaceuticals, Statistics and Data Systems, West Haven, CT 06516, USA.
authors.forename: B
authors.initials: B
authors.lastname: Emir
authors.forename: S
authors.initials: S
authors.lastname: Wieand
authors.forename: S H
authors.initials: SH
authors.lastname: Jung
authors.forename: Z
authors.initials: Z
authors.lastname: Ying
mesh_terms: D014408:Biomarkers, Tumor / Q000097:blood; D001943:Breast Neoplasms / Q000097:blood / Q000473:pathology; D002272:Carcinoembryonic Antigen / Q000097:blood; D003198:Computer Simulation; D019937:Diagnostic Techniques and Procedures / Q000706:statistics & numerical data*; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D009010:Monte Carlo Method; D018396:Mucin-1 / Q000097:blood; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric
chemical_list: D014408:Biomarkers, Tumor; D002272:Carcinoembryonic Antigen; D018396:Mucin-1