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A test for comparing conditional ROC curves with multidimensional covariates.

A Fanjul-HeviaJuan Carlos Pardo-FernándezIngrid Van KeilegomWenceslao González-Manteiga
Published in: Journal of applied statistics (2022)
The comparison of Receiver Operating Characteristic (ROC) curves is frequently used in the literature to compare the discriminatory capability of different classification procedures based on diagnostic variables. The performance of these variables can be sometimes influenced by the presence of other covariates, and thus they should be taken into account when making the comparison. A new non-parametric test is proposed here for testing the equality of two or more dependent ROC curves conditioned to the value of a multidimensional covariate. Projections are used for transforming the problem into a one-dimensional approach easier to handle. Simulations are carried out to study the practical performance of the new methodology. The procedure is then used to analyse a real data set of patients with Pleural Effusion to compare the diagnostic capability of different markers.
Keyphrases
  • systematic review
  • machine learning
  • deep learning
  • electronic health record
  • minimally invasive
  • psychometric properties
  • big data