RT Journal Article
T1 OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests
A1 López Ratón, Mónica
A1 Rodríguez Álvarez, María Xosé
A1 Cadarso Suárez, Carmen María
A1 Gude Sampedro, Francisco
K1 Optimal cutpoint
K1 Diagnostic tests
K1 Accuracy measures
K1 ROC curve
K1 R
AB 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.
PB American Statistical Association
SN 1548-7660
YR 2014
FD 2014
LK http://hdl.handle.net/10347/22021
UL http://hdl.handle.net/10347/22021
LA eng
NO López-Ratón, M., Rodríguez-Álvarez, M. X., Cadarso-Suárez, C., and Gude-Sampedro, F. (2014). OptimalCutpoints: an R package for selecting optimal cutpoints in diagnostic tests. J Stat Softw, 61(8), 1-36.
NO M. López-Ratón, M.X. Rodríguez-Alvarez and C. Cadarso-Suárez acknowledge the financial support received in the form of grants MTM2008-01603, MTM2010-09213-E and MTM2011-28285-C02-00 from Spain’s Ministry of Science & Innovation. The work of M.X. Rodríguez Álvarez was supported by Carlos III Institute of Health grant CA09/0053
DS Minerva
RD 27 abr 2026