A non-parametric test for comparing conditional ROC curves

A test for comparing conditional ROC curves with multidimensional covariates

“…In subsection 2.2 we show a methodology to test the equality of conditional ROC curves on a unidimensional problem (based on the one proposed in Fanjul-Hevia et al, 2021). Finally, in 2.3, we combine that methodology with the result obtained in 2.1 to solve our original problem with multidimensional covariates.…”

“…In order to obtain the distribution of this statistic, a bootstrap algorithm is proposed. This bootstrap algorithm is adapted from the procedure proposed in Martínez-Camblor and Corral (2012) and has been already used by Martínez-Camblor et al (2013) and by Fanjul-Hevia et al (2021) in the context of ROC curves. The key of this algorithm is that…”

“…In order to analyse the performance of the proposed methodology, simulations were run for the comparison of several dependent conditional ROC curves. On a first stage, these simulations were focused on analysing the behaviour of the unidimensional test described in Section 2.2, but we do not display them here, as they are very similar to the ones that can be found in Fanjul-Hevia et al (2021). Instead, we show the results for several scenarios (first under the null hypothesis and then under the alternative) in which we compare K ROC curves (with K P t2, 3u) conditioned to a d´dimensional covariate (with d P t2, 3u).…”

“…By comparing these conditional ROC curves instead of the standard ROC curves it is possible to incorporate the potential effect of the covariates in the analysis of the equivalence of two or more methods of diagnosis. A test for performing this comparison is proposed in Fanjul-Hevia et al (2021) for the case of a continuous one-dimensional covariate. The objective here is to extend that methodology to the case in which we have a multidimensional covariate.…”

“…In Fanjul-Hevia et al (2021) the estimation of the conditional ROC curve that is used is based on the indirect (or induced) regression methodology, which incorporates the covariate information through regression models by considering the effect of those covariates in the diagnostic marker in each population of healthy or diseased separately. However, this method was originally designed for one single covariate.…”