Abstract
The ability of a medical test to differentiate between diseased and non-diseased states is of vital importance and must be screened by statistical analysis for reliability and improvement. The receiver operating characteristic (ROC) curve remains a popular method of marker analysis, disease screening and diagnosis. Covariates in this field related to the subject’s characteristics are incorporated in the analysis to avoid bias. The covariate adjusted ROC (AROC) curve was proposed as a method of incorporation. The AROC R-package was recently released and brings various methods of estimation based on multiple authors work.
The aim of this study was to explore the AROC package functionality and usability using real data noting its possible limitations. The main methods of the package were capable of incorporating different and multiple variables, both categorical and continuous, in the AROC curve estimation. When tested for the same data, AROC curves are generated with no statistical differences, regardless of method.
The package offers a variety of methods to estimate the AROC curve complemented with predictive checks and pooled ROC estimation. The package offers a way to conduct a more thorough ROC and AROC analysis, making it available for any R user.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brito, A.S.J.D., Matsuo, T., Gonzalez, M.R.C., de Carvalho, A.B.R., Ferrari, L.S.L.: CRIB score, birth weight and gestational age in neonatal mortality risk evaluation. Revista de saúde pública 37(5), 597–602 (2003). http://www.ncbi.nlm.nih.gov/pubmed/14569335
Ezz- Eldin, Z.M., Abdel Hamid, T.A., Labib Youssef, M.R., Nabil, H.E.D.: Clinical risk index for babies (CRIB II) scoring system in prediction of mortality in premature babies. J. Clin. Diagnostic Res. 9(6), SC08–SC11 (2015). https://doi.org/10.7860/JCDR/2015/12248.6012
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., Gelman, A.: Visualization in Bayesian workflow. J. Royal Stat. Soc. Ser. A: Stat. Soc. 182(2), 389–402 (2019). https://doi.org/10.1111/rssa.12378
Gu, J., Ghosal, S., Roy, A.: Bayesian bootstrap estimation of ROC curve. Stat. Med. 27(26), 5407–5420 (2008). https://doi.org/10.1002/sim.3366, http://doi.wiley.com/10.1002/sim.3366
Hsieh, F., Turnbull, B.W.: Nonparametric and semiparametric estimation of the receiver operating characteristic curve. Ann. Statist. 24(1), 25–40 (1996). https://doi.org/10.1214/aos/1033066197
Janes, H., Pepe, M.S.: Adjusting for covariates in studies of diagnostic, screening, or prognostic markers: an old concept in a new setting. Am. J. Epidemiol. 168(1), 89–97 (2008). https://doi.org/10.1093/aje/kwn099
Janes, H., Pepe, M.S.: Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve. Biometrika 96(2), 371–382 (2009). https://doi.org/10.1093/biomet/asp002
Lasko, T.A., Bhagwat, J.G., Zou, K.H., Ohno-Machado, L.: The use of receiver operating characteristic curves in biomedical informatics 38, 404–415 (2005). https://doi.org/10.1016/j.jbi.2005.02.008
Machado, E., Costa, F., Braga, A.C.: Neonatalportugal. Mendeley Data (2019). https://doi.org/10.17632/jsmgcmfmdx.1
Mourão, M.F., Braga, A.C., Oliveira, P.N.: CRIB conditional on gender: nonparametric ROC curve. Int. J. Health Care Quality Assurance 27(8), 656–663 (2014). https://doi.org/10.1108/IJHCQA-04-2013-0047
Park, S.H., Goo, J.M., Jo, C.H.: Receiver operating characteristic (ROC) curve: practical review for radiologists. Korean J. Radiol. 5(1), 11 (2004). https://doi.org/10.3348/kjr.2004.5.1.11
Parry, G., Tucker, J., Tarnow-Mordi, W.O.: UK neonatal staffing study collaborative: CRIB II : an update of the clinical risk index for babies score For personal use. Only reproduce with permission from The Lancet Publishing Group. Lancet, 361 1789–1791 (2003). https://doi.org/10.1016/S0140-6736(03)13397-1
Pepe, M.S.: The Statistical Evaluation of Medical Tests for Classification and Prediction (2003)
Rodriguez-Alvarez, M.X., Inacio de Carvalho, V.: AROC: Covariate-Adjusted Receiver Operating Characteristic Curve Inference (2018). https://CRAN.R-project.org/package=AROC, r package version 1.0
Rodríguez-Álvarez, M.X., Roca-Pardiñas, J., Cadarso-Suárez, C.: ROC curve and covariates: extending induced methodology to the non-parametric framework. Stat. Comput. 21(4), 483–499 (2011). https://doi.org/10.1007/s11222-010-9184-1
Rodríguez-Álvarez, M.X., Roca-Pardiñas, J., Cadarso-Suárez, C., Tahoces, P.G.: Bootstrap-based procedures for inference in nonparametric receiver-operating characteristic curve regression analysis. Stat. Methods Med. Res. 27(3), 740–764 (2018). https://doi.org/10.1177/0962280217742542
Terzic, S., Heljić, S.: Assessing mortality risk in very low birth weight infants. Medicinski arhiv 66, 76–9 (2012). https://doi.org/10.5455/medarh.2012.66.76-79
Acknowledgements
This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.
The authors express their gratitude to the Portuguese National Registry for supplying the dataset used in this study.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Machado e Costa, F., Braga, A.C. (2020). Adjusting ROC Curve for Covariates with AROC R Package. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-58808-3_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58807-6
Online ISBN: 978-3-030-58808-3
eBook Packages: Computer ScienceComputer Science (R0)