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smoothROCtime: an R package for time-dependent ROC curve estimation

Author

Listed:
  • Susana Díaz-Coto

    (University of Oviedo)

  • Pablo Martínez-Camblor

    (Dartmouth College)

  • Sonia Pérez-Fernández

    (University of Oviedo)

Abstract

The receiver operating characteristic (ROC) curve has become one of the most used tools for analyzing the diagnostic capacity of continuous biomarkers. When the studied outcome is a time-dependent variable two main generalizations have been proposed, based on properly extensions of the sensitivity and the specificity. Different procedures have been suggested for their estimation mainly under the presence of right censorship. Most of them have been implemented, as well, in diverse types of software, including R packages. This work focuses on the R implementation for the smooth estimation of time-dependent ROC curves. The theoretical connection between them through the joint distribution function of the biomarker and time-to-event variables prompts an approximation method: considered estimators are based on the bivariate kernel density estimator for the joint density function of the bidimensional variable (Marker, Time-to-event). The use of the package is illustrated with two real-world examples.

Suggested Citation

  • Susana Díaz-Coto & Pablo Martínez-Camblor & Sonia Pérez-Fernández, 2020. "smoothROCtime: an R package for time-dependent ROC curve estimation," Computational Statistics, Springer, vol. 35(3), pages 1231-1251, September.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:3:d:10.1007_s00180-020-00955-7
    DOI: 10.1007/s00180-020-00955-7
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    References listed on IDEAS

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    5. Patrick J. Heagerty & Thomas Lumley & Margaret S. Pepe, 2000. "Time-Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker," Biometrics, The International Biometric Society, vol. 56(2), pages 337-344, June.
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