Facultade de Fisioterapia

Boostrap-based procedures for inference in nonparametric ROC regression analysis

Rodríguez Álvarez, María Xosé; Roca Pardiñas, Javier; Cadarso Suárez, C.; Tahoces, P.G.
Before the use of a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is an essential step. The receiver operating characteristic (ROC) curve is the measure of accuracy most widely used for continuous diagnostic tests. However, the possible impact of extra information about the patient (or even the environment) on diagnostic accuracy needs to be also assessed. In this paper, attention is focused on an estimator for the covariate-specific ROC curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of generalized additive models for the ROC curve (ROC-GAM). The main aim of the paper is to offer new inferential procedures for testing the effect of covariates over the conditional ROC curve within the ROC-GAM context. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of continuous covariates on the ROC curve; and (b) the presence of factor-by-curve interaction terms. The validity of the proposed bootstrap-based procedures is supported by simulations. To facilitate the application of these new procedures in practice, an R-package, known as npROCRegression, is provided and briefly described. Finally, data derived from a computed-aided diagnostic (CAD) system for the automatic detection of tumour masses in breast cancer is analysed.
Type of Publication:
Technical Report
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This research was supported by the Spanish Ministry of Economy and Competitiveness MINECO grants MTM2014-55966-P, MTM2014-52975-C2-1-R and BCAM Severo Ochoa excellence accreditation SEV-2013-0323, and by the Basque Government through the BERC 360 2014–2017.
Hits: 1878