title: Bayesian estimation of the receiver operating characteristic curve for a diagnostic test with a limit of detection in the absence of a gold standard.
abstract: The receiver operating characteristic (ROC) curve is commonly used for evaluating the discriminatory ability of a biomarker. Measurements for a diagnostic test may be subject to an analytic limit of detection leading to immeasurable or unreportable test results. Ignoring the scores that are beyond the limit of detection of a test leads to a biased assessment of its discriminatory ability, as reflected by indices such as the associated area under the curve (AUC). We propose a Bayesian approach for the estimation of the ROC curve and its AUC for a test with a limit of detection in the absence of gold standard based on assumptions of normally and gamma-distributed data. The methods are evaluated in simulation studies, and a truncated gamma model with a point mass is used to evaluate quantitative real-time polymerase chain reaction data for bovine Johne's disease (paratuberculosis). Simulations indicated that estimates of diagnostic accuracy and AUC were good even for relatively small sample sizes (n=200). Exceptions were when there was a high per cent of unquantifiable results (60 per cent) or when AUC was < or =0.6, which indicated a marked overlap between the outcomes in infected and non-infected populations.
authors.affiliation: Department of Medicine and Epidemiology, University of California, Davis, CA 95616, USA. srjafarzadeh@ucdavis.edu
authors.forename: Seyed Reza
authors.initials: SR
authors.lastname: Jafarzadeh
authors.forename: Wesley O
authors.initials: WO
authors.lastname: Johnson
authors.forename: Jessica M
authors.initials: JM
authors.lastname: Utts
authors.forename: Ian A
authors.initials: IA
authors.lastname: Gardner
mesh_terms: D000818:Animals; D019540:Area Under Curve; D001499:Bayes Theorem; D056808:Biostatistics; D002417:Cattle; D002418:Cattle Diseases / Q000175:diagnosis / Q000382:microbiology; D003955:Diagnostic Tests, Routine / Q000592:standards / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016927:Mycobacterium avium subsp. paratuberculosis / Q000235:genetics / Q000302:isolation & purification; D010283:Paratuberculosis / Q000175:diagnosis / Q000382:microbiology; D016133:Polymerase Chain Reaction / Q000706:statistics & numerical data / Q000662:veterinary; D012372:ROC Curve