Model checking for ROC regression analysis.
The receiver operating characteristic (ROC) curve is a prominent tool for characterizing the accuracy of a continuous diagnostic test. To account for factors that might influence the test accuracy, various ROC regression methods have been proposed. However, as in any regression analysis, when the assumed models do not fit the data well, these methods may render invalid and misleading results. To date, practical model-checking techniques suitable for validating existing ROC regression models are not yet available. In this article, we develop cumulative residual-based procedures to graphically and numerically assess the goodness of fit for some commonly used ROC regression models, and show how specific components of these models can be examined within this framework. We derive asymptotic null distributions for the residual processes and discuss resampling procedures to approximate these distributions in practice. We illustrate our methods with a dataset from the cystic fibrosis registry.
Cai
Tianxi
T
Department of Biostatistics, Harvard University, Boston, Massachusetts 02115, USA. tcai@hsph.harvard.edu
Zheng
Yingye
Y
D001827:Body Height; D003550:Cystic Fibrosis / Q000453:epidemiology; D006801:Humans; D008499:Medical Records; D015233:Models, Statistical; D012372:ROC Curve; D012016:Reference Values; D012042:Registries; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D014861:Washington