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Estimation and comparison of receiver operating characteristic curves

Author

Listed:
  • Margaret S. Pepe

    (Fred Hutchinson Cancer Research Center)

  • Gary Longton

    (Fred Hutchinson Cancer Research Center)

  • Holly Janes

    (Fred Hutchinson Cancer Research Center)

Abstract

The receiver operating characteristic (ROC) curve displays the capacity of a marker or diagnostic test to discriminate between two groups of subjects, cases versus controls. We present a comprehensive suite of Stata commands for performing ROC analysis. Nonparametric, semiparametric, and parametric estimators are calculated. Comparisons between curves are based on the area or partial area under the ROC curve. Alternatively, pointwise comparisons between ROC curves or inverse ROC curves can be made. We describe options to adjust these analyses for covariates and to perform ROC regression in a companion article. We use a unified framework by representing the ROC curve as the distribution of the marker in cases where we have standardized it to the control reference distribution. Copyright 2009 by StataCorp LP.

Suggested Citation

  • Margaret S. Pepe & Gary Longton & Holly Janes, 2009. "Estimation and comparison of receiver operating characteristic curves," Stata Journal, StataCorp LP, vol. 9(1), pages 1-16, March.
    • Handle: RePEc:tsj:stataj:v:9:y:2009:i:1:p:1-16
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    References listed on IDEAS

    as
    1. Lori E. Dodd & Margaret S. Pepe, 2003. "Partial AUC Estimation and Regression," Biometrics, The International Biometric Society, vol. 59(3), pages 614-623, September.
    2. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
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    1. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
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    6. Detken, Carsten & Weeken, Olaf & Alessi, Lucia & Bonfim, Diana & Boucinha, Miguel & Castro, Christian & Frontczak, Sebastian & Giordana, Gaston & Giese, Julia & Wildmann, Nadya & Kakes, Jan & Klaus, B, 2014. "Operationalising the countercyclical capital buffer: indicator selection, threshold identification and calibration options," ESRB Occasional Paper Series 5, European Systemic Risk Board.
    7. Aastveit, Knut Are & Anundsen, André K. & Herstad, Eyo I., 2019. "Residential investment and recession predictability," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1790-1799.
    8. Elizabeth Gutierrez & Jake Krupa & Miguel Minutti-Meza & Maria Vulcheva, 2020. "Do going concern opinions provide incremental information to predict corporate defaults?," Review of Accounting Studies, Springer, vol. 25(4), pages 1344-1381, December.
    9. Holly Janes & Gary Longton & Margaret S. Pepe, 2009. "Accommodating covariates in receiver operating characteristic analysis," Stata Journal, StataCorp LP, vol. 9(1), pages 17-39, March.
    10. Xinyue Wu & Hong Zhu & Liuru Hu & Jian Meng & Fulu Sun, 2024. "Analysis of Short-Term Heavy Rainfall-Based Urban Flood Disaster Risk Assessment Using Integrated Learning Approach," Sustainability, MDPI, vol. 16(18), pages 1-19, September.
    11. Oke Gerke & Antonia Zapf, 2022. "Convergence Behavior of Optimal Cut-Off Points Derived from Receiver Operating Characteristics Curve Analysis: A Simulation Study," Mathematics, MDPI, vol. 10(22), pages 1-14, November.
    12. Bespalova, Olga, 2018. "Forecast Evaluation in Macroeconomics and International Finance. Ph.D. thesis, George Washington University, Washington, DC, USA," MPRA Paper 117706, University Library of Munich, Germany.
    13. Jesús F. Lampón & Pablo Cabanelas-Lorenzo & Santiago Lago-Peñas, 2013. "Why firms relocate their production overseas? The answer lies inside: corporate, logistic and technological determinants," Working Papers 2013/3, Institut d'Economia de Barcelona (IEB).
    14. Mund, Carolin & Neuhäusler, Peter, 2015. "Towards an early-stage identification of emerging topics in science—The usability of bibliometric characteristics," Journal of Informetrics, Elsevier, vol. 9(4), pages 1018-1033.
    15. Marcin Łupiński, 2019. "Wskaźniki wczesnego ostrzegania przed niestabilnością finansową polskiego sektora bankowego," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 55, pages 99-113.
    16. Geršl, Adam & Jašová, Martina, 2018. "Credit-based early warning indicators of banking crises in emerging markets," Economic Systems, Elsevier, vol. 42(1), pages 18-31.
    17. Nancy Puttkammer & Steven Zeliadt & Jean Gabriel Balan & Janet Baseman & Rodney Destiné & Jean Wysler Domerçant & Garilus France & Nathaelf Hyppolite & Valérie Pelletier & Nernst Atwood Raphael & Kenn, 2014. "Development of an Electronic Medical Record Based Alert for Risk of HIV Treatment Failure in a Low-Resource Setting," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-12, November.
    18. Rocío Aznar-Gimeno & Luis M. Esteban & Gorka Labata-Lezaun & Rafael del-Hoyo-Alonso & David Abadia-Gallego & J. Ramón Paño-Pardo & M. José Esquillor-Rodrigo & Ángel Lanas & M. Trinidad Serrano, 2021. "A Clinical Decision Web to Predict ICU Admission or Death for Patients Hospitalised with COVID-19 Using Machine Learning Algorithms," IJERPH, MDPI, vol. 18(16), pages 1-20, August.
    19. Eva V Applebaum & Dominic Breton & Zhuo Wei Feng & An-Tchi Ta & Kayley Walsh & Kathleen Chassé & Shawn M Robbins, 2017. "Modified 30-second Sit to Stand test predicts falls in a cohort of institutionalized older veterans," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-13, May.
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