A Bayesian hierarchical non-linear regression model in receiver operating characteristic analysis of clustered
Kelly H Zou1, A James O'Malley
1Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115, USA. zou@bwh.harvard.edu
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.
Related Concept Videos
Receiver Operating Characteristic Plot
Statistical Methods for Analyzing Epidemiological Data
Comparing the Survival Analysis of Two or More Groups
Sensitivity, Specificity, and Predicted Value
Sensitivity is the...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...

