OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests

By Mónica López-Ratón, María Xosé Rodríguez-Álvarez, Carmen Cadarso-Suárez, F. Gude-Sampedro. 2014

598 citations recorded by the source corpus.

Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For the clinical application of such tests, it is useful to select a cutpoint or discrimination value c that defines positive and negative test results. In general, individuals with a diagnostic test value of c or higher are classified as diseased. Several search strategies have been proposed for choosing optimal cutpoints in diagnostic tests, depending on the underlying reason for this choice. This paper introduces an R package, known as OptimalCutpoints, for selecting optimal cutpoints in diagnostic tests. It incorporates criteria that take the costs of the different diagnostic decisions into account, as well as the prevalence of the target disease and several methods based on measures of diagnostic test accuracy. Moreover, it enables optimal levels to be calculated according to levels of given (categorical) covariates. While the numerical output includes the optimal cutpoint values and associated accuracy measures with their confidence intervals, the graphical output includes the receiver operating characteristic (ROC) and predictive ROC curves. An illustration of the use of OptimalCutpoints is provided, using a real biomedical dataset.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/41bf387d83a4dde5c726f1f6ad750ffda98f6f14

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Citations in the corpus

Receiver operating characteristic curve: overview and practical use for clinicians (2022, methodology): “OptimalCutpoints is a sophisticated R package specially developed to determine the optimal cut-off value.”
cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R (2020, methodology): “To oﬀer a comparison to established solutions, cutpointr 1.0.0 was benchmarked against optimal.cutpoints from OptimalCutpoints 1.1-4 (L´opez-Rat´on et al. 2014), thres2 from ThresholdROC 2.7 (Perez-Jaume et al. 2017), a custom function using the functions prediction and performance from ROCR 1.0-7…”
easyROC: An Interactive Web-tool for ROC Curve Analysis Using R Language Environment (2016, methodology): “We made use of the OptimalCutPoints package from R (Lopez-Raton et al., 2014), which has 34 different methods, to calculate cutpoints for each marker.”