IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v115y2017icp1-10.html
   My bibliography  Save this article

Smoothed empirical likelihood for the Youden index

Author

Listed:
  • Wang, Dongliang
  • Tian, Lili
  • Zhao, Yichuan

Abstract

For a continuous scale biomarker of binary disease status, the Youden index is a frequently used measurement of diagnostic accuracy in the context of the receiver operating characteristic curve and provides an optimal threshold for making diagnosis. The majority of existing inference methods for the Youden index are either parametric or bootstrap based. In the current paper, the empirical likelihood method for the Youden index is derived via defining novel smoothed estimating equations, and Wilks’ theorem for the empirical likelihood ratio statistic is established. Extensive simulation studies suggest that the chi-square calibrated empirical likelihood interval estimators are robust to model assumptions, enjoy computational efficiency and perform better than the bootstrap procedure almost uniformly across a variety of scenarios in terms of coverage probabilities.

Suggested Citation

  • Wang, Dongliang & Tian, Lili & Zhao, Yichuan, 2017. "Smoothed empirical likelihood for the Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 1-10.
    • Handle: RePEc:eee:csdana:v:115:y:2017:i:c:p:1-10
    DOI: 10.1016/j.csda.2017.03.014
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947317300592
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2017.03.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Lai, Chin-Ying & Tian, Lili & Schisterman, Enrique F., 2012. "Exact confidence interval estimation for the Youden index and its corresponding optimal cut-point," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1103-1114.
    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. Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.

    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. 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.
    2. Poon, Wai-Yin & Qiu, Shi-Fang & Tang, Man-Lai, 2015. "Confidence interval construction for the Youden index based on partially validated series," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 116-134.
    3. Rota, Matteo & Antolini, Laura, 2014. "Finding the optimal cut-point for Gaussian and Gamma distributed biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 1-14.
    4. Yin, Jingjing & Tian, Lili, 2014. "Joint inference about sensitivity and specificity at the optimal cut-off point associated with Youden index," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 1-13.
    5. Qiu, Zhiping & Peng, Limin & Manatunga, Amita & Guo, Ying, 2019. "A smooth nonparametric approach to determining cut-points of a continuous scale," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 186-210.

    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:115:y:2017:i:c:p:1-10. 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.