title: A Bayesian hierarchical non-linear regression model in receiver operating characteristic analysis of clustered continuous diagnostic data.
abstract: Receiver operating characteristic (ROC) analysis is a useful evaluative method of diagnostic accuracy. A Bayesian hierarchical nonlinear regression model for ROC analysis was developed. A validation analysis of diagnostic accuracy was conducted using prospective multi-center clinical trial prostate cancer biopsy data collected from three participating centers. The gold standard was based on radical prostatectomy to determine local and advanced disease. To evaluate the diagnostic performance of PSA level at fixed levels of Gleason score, a normality transformation was applied to the outcome data. A hierarchical regression analysis incorporating the effects of cluster (clinical center) and cancer risk (low, intermediate, and high) was performed, and the area under the ROC curve (AUC) was estimated.
authors.affiliation: Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115, USA. zou@bwh.harvard.edu
authors.forename: Kelly H
authors.initials: KH
authors.lastname: Zou
authors.forename: A James
authors.initials: AJ
authors.lastname: O'Malley
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D011471:Prostatic Neoplasms / Q000473:pathology; D012372:ROC Curve; D012044:Regression Analysis; D015203:Reproducibility of Results; D012107:Research Design; D012680:Sensitivity and Specificity