Nonparametric Covariate Adjustment
for Receiver Operating Characteristic Curves
Radu Craiu
Associate Professor of Statistics
University of Toronto, Canada
Friday, January 25, 2008
12:00 pm
5275 MSC
| ABSTRACT |
The accuracy of a diagnostic test is typically characterized using the
receiver operating characteristic (ROC) curve. Summarizing indexes such as the area under the ROC curve (AUC) are used to compare different tests as well as to measure the difference between two populations. Often additional information is available on some of the covariates which are known to influence the accuracy of such measures. We propose nonparametric methods for covariate adjustment of the AUC. Models with normal errors and non-normal errors are discussed and analyzed separately. Nonparametric
regression is used for estimating mean and variance functions in both
scenarios. In the general noise distribution case we propose a
covariate-adjusted Mann-Whitney estimator for AUC estimation which
effectively uses available data to construct working samples at any
covariate value of interest and is computationally efficient for
implementation. This provides a generalization of the Mann-Whitney
approach for comparing two populations by taking covariate effects into account. The usefulness of the proposed methods is demonstrated through simulated and real data examples.
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