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Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

Author

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  • Manuel Franco

    (Department of Statistics and Operations Research, University of Murcia, CEIR Campus Mare Nostrum, 30100 Murcia, Spain
    Bio-Health Research Institute of Murcia (IMIB-Arrixaca), 30120 Murcia, Spain)

  • Juana-María Vivo

    (Department of Statistics and Operations Research, University of Murcia, CEIR Campus Mare Nostrum, 30100 Murcia, Spain
    Bio-Health Research Institute of Murcia (IMIB-Arrixaca), 30120 Murcia, Spain)

Abstract

The burgeoning advances in high-throughput technologies have posed a great challenge to the identification of novel biomarkers for diagnosing, by contemporary models and methods, through bioinformatics-driven analysis. Diagnostic performance metrics such as the partial area under the R O C ( p A U C ) indexes exhibit limitations to analysing genomic data. Among other issues, the inability to differentiate between biomarkers whose R O C curves cross each other with the same p A U C value, the inappropriate expression of non-concave R O C curves, and the lack of a convenient interpretation, restrict their use in practice. Here, we have proposed the fitted partial area index ( F p A U C ), which is computable through an algorithm valid for any R O C curve shape, as an alternative performance summary for the evaluation of highly sensitive biomarkers. The proposed approach is based on fitter upper and lower bounds of the p A U C in a high-sensitivity region. Through variance estimates, simulations, and case studies for diagnosing leukaemia, and ovarian and colon cancers, we have proven the usefulness of the proposed metric in terms of restoring the interpretation and improving diagnostic accuracy. It is robust and feasible even when the R O C curve shows hooks, and solves performance ties between competitive biomarkers.

Suggested Citation

  • Manuel Franco & Juana-María Vivo, 2021. "Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:21:p:2826-:d:673830
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    References listed on IDEAS

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    1. James A. Hanley, 1988. "The Robustness of the "Binormal" Assumptions Used in Fitting ROC Curves," Medical Decision Making, , vol. 8(3), pages 197-203, August.
    2. Barbara J. McNeil & James A. Hanley, 1984. "Statistical Approaches to the Analysis of Receiver Operating Characteristic (ROC) Curves," Medical Decision Making, , vol. 4(2), pages 137-150, June.
    3. Juana-María Vivo & Manuel Franco & Donatella Vicari, 2018. "Rethinking an ROC partial area index for evaluating the classification performance at a high specificity range," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 683-704, September.
    4. Peterson, A. Townsend & Papeş, Monica & Soberón, Jorge, 2008. "Rethinking receiver operating characteristic analysis applications in ecological niche modeling," Ecological Modelling, Elsevier, vol. 213(1), pages 63-72.
    5. Dudoit S. & Fridlyand J. & Speed T. P, 2002. "Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 77-87, March.
    6. Margaret Sullivan Pepe & Gary Longton & Garnet L. Anderson & Michel Schummer, 2003. "Selecting Differentially Expressed Genes from Microarray Experiments," Biometrics, The International Biometric Society, vol. 59(1), pages 133-142, March.
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