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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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)
Collinson, P.: Of bombers, radiologists and cardiologists: time to ROC. Heart 80, 215–217 (1998)
Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)
Flack, P.: ROC analysis. In: Encyclopedia of Machine Learning, 1 edn., pp. 869–874. Springer (2011)
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
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)
Geer, L.Y., Marchler-Bauer, A., Geer, R.C., et al.: The NCBI BioSystems database. Nucleic Acids Res. 38(Database), D492–D496 (2009)
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
Gonen, M.: Receiver Operating Characteristic (ROC) Curves (Paper 210-31). SUGI 31 Proceedings, pp. 1–18. SAS Institute Inc. (2006)
Krzanowski, W.J., Hand, D.J.: ROC Curves for Continuous Data. CRC Press, Boca Raton (2009)
Liu, H., Li, J., Wong, L.: Use of extreme patient samples for outcome prediction from gene expression data. Bioinformatics 21(16), 3377–3384 (2005)
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)
Sammut, C., Webb, G.: Encyclopedia of Machine Learning. Springer (2011)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Santos, C., Braga, A.C. (2017). ROC632: An Overview. In: Fdez-Riverola, F., Mohamad, M., Rocha, M., De Paz, J., Pinto, T. (eds) 11th International Conference on Practical Applications of Computational Biology & Bioinformatics. PACBB 2017. Advances in Intelligent Systems and Computing, vol 616. Springer, Cham. https://doi.org/10.1007/978-3-319-60816-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-60816-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60815-0
Online ISBN: 978-3-319-60816-7
eBook Packages: EngineeringEngineering (R0)