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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    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.
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