ROC632: An Overview

Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 616)

Abstract

The present paper aims to analyze and explore the ROC632 package, specifying its main characteristics and functions. More specifically, the goal of this study is the evaluation of the effectiveness of the package and its strengths and weaknesses. This package was created in order to overcome the lack of information concerning incomplete time-to-event data, adapting the 0.632+ bootstrap estimator for the evaluation of time dependent ROC curves. By applying this package to a specific dataset (DLBCLpatients), it becomes possible to assess tangible data, determining if it is able to analyze complete and incomplete data efficiently and without bias.

Keywords

ROC632 package 0.632\(+\) bootstrap ROC curves 
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References

  1. 1.
    Ambroise, C., McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. Proc. Nat. Acad. Sci. 99(10), 6562–6566 (2002)CrossRefMATHGoogle Scholar
  2. 2.
    Collinson, P.: Of bombers, radiologists and cardiologists: time to ROC. Heart 80, 215–217 (1998)CrossRefGoogle Scholar
  3. 3.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Flack, P.: ROC analysis. In: Encyclopedia of Machine Learning, 1 edn., pp. 869–874. Springer (2011)Google Scholar
  5. 5.
    Foucher, Y.: ROC632: construction of diagnostic or prognostic scoring system and internal validation of its discriminative capacities based on ROC curve and 0.633\(+\) bootstrap resampling, R package version 0.6 (2013). https://cran.r-project.org/web/packages/ROC632/index.html
  6. 6.
    Foucher, Y., Danger, R.: Time dependent ROC curves for the estimation of true prognostic capacity of microarray data. Stat. Appl. Genet. Mol. Biol. 11(6), 1 (2012)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Geer, L.Y., Marchler-Bauer, A., Geer, R.C., et al.: The NCBI BioSystems database. Nucleic Acids Res. 38(Database), D492–D496 (2009)Google Scholar
  8. 8.
    Goeman, J., Meijer, R., Chaturvedi, N.: L1 (Lasso and Fused Lasso) and L2 (Ridge) Penalized Estimation, R package version 0.9-47 (2013). https://cran.r-project.org/web/packages/penalized/index.html
  9. 9.
    Gonen, M.: Receiver Operating Characteristic (ROC) Curves (Paper 210-31). SUGI 31 Proceedings, pp. 1–18. SAS Institute Inc. (2006)Google Scholar
  10. 10.
    Krzanowski, W.J., Hand, D.J.: ROC Curves for Continuous Data. CRC Press, Boca Raton (2009)CrossRefMATHGoogle Scholar
  11. 11.
    Liu, H., Li, J., Wong, L.: Use of extreme patient samples for outcome prediction from gene expression data. Bioinformatics 21(16), 3377–3384 (2005)CrossRefGoogle Scholar
  12. 12.
    Rosenwald, A., Wright, G., Chan, W.C., et al.: The use of molecular profiling to predict survival after chemotherapy for diffuse large-b-cell lymphoma. N. Engl. J. Med. 346(25), 1937–1947 (2002)CrossRefGoogle Scholar
  13. 13.
    Sammut, C., Webb, G.: Encyclopedia of Machine Learning. Springer (2011)Google Scholar
  14. 14.
    Steyerberg, E.W., Harrell, F.E., Borsboom, G.J., et al.: Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J. Clin. Epidemiol. 54(8), 774–781 (2001)CrossRefGoogle Scholar
  15. 15.
    Vu, T., Sima, C., Braga-Neto, U.M., Dougherty, E.R.: Unbiased bootstrap error estimation for linear discriminant analysis. J. Bioinf. Syst. Biol. 2014(1), 1–15 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.University of MinhoBragaPortugal
  2. 2.Algoritmi CentreUniversity of MinhoBragaPortugal

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