Accommodating covariates in receiver operating characteristic analysis

Among the advantages of this method are the following: (i) ensuring that results from regression and performance assessments are consistent with each other; (ii) allowing covariate adjustment and covariate effects on ROC curves to be handled in a familiar way, and (iii) providing a mechanism to incorporate important assumptions about structure in the ROC curve into the fitted risk model.

We develop the method in detail for the problem of combining biomarker data sets derived from multiple studies, populations or biomarker measurement platforms, when ROC curves are similar across data sources.

Based on the notion of using a threshold to classify subjects as positive (diseased) or negative (nondiseased), an ROC curve is a plot of the true-positive rate (TPR) versus the false-positive rate (FPR) for all possible cutpoints.Disease severity was evaluated by the amount of neuroretinal rim loss assessed by confocal scanning laser ophthalmoscopy.An ROC regression model was fitted to evaluate the influence of disease severity and age on the diagnostic performance of the pattern SD (PSD) index from FDT 24-2 and SAP SITA.Our approach allows for the entire distribution in each group to change as a function of the covariates, provides exact posterior inference up to a Monte Carlo error, and can easily accommodate multiple continuous and categorical predictors. Simulation results suggest that, regarding the mean squared error, our approach performs better than its competitors for small sample sizes and nonlinear scenarios. Dates First available in Project Euclid: 9 September 2013Permanent link to this documenthttps://projecteuclid.org/euclid.ba/1378729922Digital Object Identifierdoi:10.1214/13-BA825Mathematical Reviews number (Math Sci Net) MR3102228Zentralblatt MATH identifier1329.62154Keywords Conditional area under the curve related probability distributions dependent Dirichlet process Markov chain Monte Carlo Inácio de Carvalho, Vanda; Jara, Alejandro; E.

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In this paper we present a method that simultaneously accomplishes both tasks.

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