IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2008i1p233-245.html
   My bibliography  Save this article

Comparing correlated ROC curves for continuous diagnostic tests under density ratio models

Author

Listed:
  • Wan, Shuwen
  • Zhang, Biao

Abstract

A family of nonparametric statistics to comparing ROC curves for continuous diagnostic tests was proposed by Wieand et al. [Wieand, S., Gail, M.H., James, B.R., James, K.L., 1989. A family of nonparametric statistics for comparing diagnostic markers with paired or unpaired data. Biometrika 76, 585-592]. In this paper, we study the semiparametric counterpart. We propose a two-sample semiparametric bivariate density ratio model, under which new ROC curve estimators are constructed and a family of semiparametric statistics for comparing ROC curves are proposed. We derive the asymptotic results on the newly proposed ROC curve estimators and show that they are more efficient than the nonparametric counterparts. We also show the proposed method for comparing ROC curves is more efficient than the nonparametric counterpart. A simulation study and the analysis of two real examples are also presented.

Suggested Citation

  • Wan, Shuwen & Zhang, Biao, 2008. "Comparing correlated ROC curves for continuous diagnostic tests under density ratio models," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 233-245, September.
    • Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:233-245
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00354-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Biao, 2006. "A semiparametric hypothesis testing procedure for the ROC curve area under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1855-1876, April.
    2. Donna Katzman McClish, 1987. "Comparing the Areas under More Than Two Independent ROC Curves," Medical Decision Making, , vol. 7(3), pages 149-155, August.
    3. Jing Qin, 2003. "Using logistic regression procedures for estimating receiver operating characteristic curves," Biometrika, Biometrika Trust, vol. 90(3), pages 585-596, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Suohong & Zhang, Biao, 2014. "Semiparametric empirical likelihood confidence intervals for AUC under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 101-115.
    2. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Siyan Liu & Qinglong Tian & Yukun Liu & Pengfei Li, 2024. "Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model," Mathematics, MDPI, vol. 12(13), pages 1-21, July.
    2. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    3. Cheam, Amay S.M. & McNicholas, Paul D., 2016. "Modelling receiver operating characteristic curves using Gaussian mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 192-208.
    4. B Rey deCastro, 2019. "Cumulative ROC curves for discriminating three or more ordinal outcomes with cutpoints on a shared continuous measurement scale," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-16, August.
    5. Alicja Jokiel-Rokita & Rafał Topolnicki, 2019. "Minimum distance estimation of the binormal ROC curve," Statistical Papers, Springer, vol. 60(6), pages 2161-2183, December.
    6. Wang, Suohong & Zhang, Biao, 2014. "Semiparametric empirical likelihood confidence intervals for AUC under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 101-115.
    7. Roy M. Poses & Randall D. Cebul & Robert M. Centor, 1988. "Eualuating Physicians' Probabilistic Judgments," Medical Decision Making, , vol. 8(4), pages 233-240, December.
    8. Zhong Guan & Cheng Peng, 2011. "A rank-based empirical likelihood approach to two-sample proportional odds model and its goodness of fit," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 23(3), pages 763-780.
    9. William M. Briggs & Russell Zaretzki, 2008. "The Skill Plot: A Graphical Technique for Evaluating Continuous Diagnostic Tests," Biometrics, The International Biometric Society, vol. 64(1), pages 250-256, March.
    10. Luo, Jingqin & Xiong, Chengjie, 2012. "DiagTest3Grp: An R Package for Analyzing Diagnostic Tests with Three Ordinal Groups," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i03).
    11. Zhang, Biao, 2006. "A semiparametric hypothesis testing procedure for the ROC curve area under a density ratio model," Computational Statistics & Data Analysis, Elsevier, vol. 50(7), pages 1855-1876, April.
    12. Gigliarano, Chiara & Figini, Silvia & Muliere, Pietro, 2014. "Making classifier performance comparisons when ROC curves intersect," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 300-312.
    13. Jiang, Shan & Tu, Dongsheng, 2012. "Inference on the probability P(T1," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1069-1078.
    14. Margaret Sullivan Pepe, 2008. "Discussions," Biometrics, The International Biometric Society, vol. 64(1), pages 256-258, March.
    15. Yousef, Waleed A. & Kundu, Subrata & Wagner, Robert F., 2009. "Nonparametric estimation of the threshold at an operating point on the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4370-4383, October.
    16. Gu Wen & Pepe Margaret, 2009. "Measures to Summarize and Compare the Predictive Capacity of Markers," The International Journal of Biostatistics, De Gruyter, vol. 5(1), pages 1-49, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:233-245. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.