title: Bayesian semiparametric estimation of covariate-dependent ROC curves.
abstract: Receiver operating characteristic (ROC) curves are widely used to measure the discriminating power of medical tests and other classification procedures. In many practical applications, the performance of these procedures can depend on covariates such as age, naturally leading to a collection of curves associated with different covariate levels. This paper develops a Bayesian heteroscedastic semiparametric regression model and applies it to the estimation of covariate-dependent ROC curves. More specifically, our approach uses Gaussian process priors to model the conditional mean and conditional variance of the biomarker of interest for each of the populations under study. The model is illustrated through an application to the evaluation of prostate-specific antigen for the diagnosis of prostate cancer, which contrasts the performance of our model against alternative models.
authors.affiliation: Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA 95064, USA.
authors.forename: Abel
authors.initials: A
authors.lastname: Rodríguez
authors.forename: Julissa C
authors.initials: JC
authors.lastname: Martínez
mesh_terms: D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood; D006801:Humans; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D017430:Prostate-Specific Antigen / Q000097:blood; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis; D012372:ROC Curve; D012044:Regression Analysis
chemical_list: D015415:Biomarkers; D017430:Prostate-Specific Antigen
keywords: Bayesian inference; Gaussian process; Non-parametric regression; Receiver operating characteristic curve