Prior to using a diagnostic test in a routine clinical setting, the rigorous evaluation of its diagnostic accuracy is essential.
The receiver-operating characteristic 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 also
needs to be assessed. In this paper, we focus on an estimator for the covariate-specific receiver-operating characteristic
curve based on direct regression modelling and nonparametric smoothing techniques. This approach defines the class of
generalised additive models for the receiver-operating characteristic curve. The main aim of the paper is to offer new
inferential procedures for testing the effect of covariates on the conditional receiver-operating characteristic curve within
the above-mentioned class. Specifically, two different bootstrap-based tests are suggested to check (a) the possible effect of
continuous covariates on the receiver-operating characteristic 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 computer-aided diagnostic system for the automatic detection of tumour masses in breast cancer is
analyse