IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v189y2024ics0167947323001512.html
   My bibliography  Save this article

Likelihood-type confidence regions for optimal sensitivity and specificity of a diagnostic test

Author

Listed:
  • Adimari, Gianfranco
  • To, Duc-Khanh
  • Chiogna, Monica
  • Scatozza, Francesca
  • Facchiano, Antonio

Abstract

New methods are proposed that provide approximate joint confidence regions for the optimal sensitivity and specificity of a diagnostic test, i.e., sensitivity and specificity corresponding to the optimal cutpoint as defined by the Youden index criterion. Such methods are semi-parametric or non-parametric and attempt to overcome the limitations of alternative approaches. The proposed methods are based on empirical likelihood pivots, giving rise to likelihood-type regions with no predetermined constraints on the shape and automatically range-respecting. The proposal covers three situations: the binormal model, the binormal model after the use of Box-Cox transformations and the fully non-parametric model. In the second case, it is also shown how to use two different transformations, for the healthy and the diseased subjects. The finite sample behaviour of our methods is investigated using simulation experiments. The simulation results also show the advantages offered by our methods when compared with existing competitors. Illustrative examples, involving three real datasets, are also provided.

Suggested Citation

  • Adimari, Gianfranco & To, Duc-Khanh & Chiogna, Monica & Scatozza, Francesca & Facchiano, Antonio, 2024. "Likelihood-type confidence regions for optimal sensitivity and specificity of a diagnostic test," Computational Statistics & Data Analysis, Elsevier, vol. 189(C).
    • Handle: RePEc:eee:csdana:v:189:y:2024:i:c:s0167947323001512
    DOI: 10.1016/j.csda.2023.107840
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947323001512
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2023.107840?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. Leonidas E. Bantis & Christos T. Nakas & Benjamin Reiser, 2014. "Construction of confidence regions in the ROC space after the estimation of the optimal Youden index-based cut-off point," Biometrics, The International Biometric Society, vol. 70(1), pages 212-223, March.
    2. Adimari Gianfranco & Chiogna Monica, 2010. "Simple Nonparametric Confidence Regions for the Evaluation of Continuous-Scale Diagnostic Tests," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-20, July.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Jianfang Cao & Yanfei Li & Yun Tian, 2018. "Emotional modelling and classification of a large-scale collection of scene images in a cluster environment," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    2. Rocío Aznar-Gimeno & Luis M. Esteban & Rafael del-Hoyo-Alonso & Ángel Borque-Fernando & Gerardo Sanz, 2022. "A Stepwise Algorithm for Linearly Combining Biomarkers under Youden Index Maximization," Mathematics, MDPI, vol. 10(8), pages 1-26, April.
    3. Vanda Inácio de Carvalho & Miguel de Carvalho & Adam J. Branscum, 2017. "Nonparametric Bayesian covariate‐adjusted estimation of the Youden index," Biometrics, The International Biometric Society, vol. 73(4), pages 1279-1288, December.
    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. Rocío Aznar-Gimeno & Luis M. Esteban & Gerardo Sanz & Rafael del-Hoyo-Alonso & Ricardo Savirón-Cornudella, 2021. "Incorporating a New Summary Statistic into the Min–Max Approach: A Min–Max–Median, Min–Max–IQR Combination of Biomarkers for Maximising the Youden Index," Mathematics, MDPI, vol. 9(19), pages 1-17, October.
    6. 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.

    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:189:y:2024:i:c:s0167947323001512. 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.