IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i21p2826-d673830.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/21/2826/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/9/21/2826/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. 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.
    2. Bickel David R., 2008. "Correcting the Estimated Level of Differential Expression for Gene Selection Bias: Application to a Microarray Study," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-27, March.
    3. Wiltshire, Kathryn H & Tanner, Jason E, 2020. "Comparing maximum entropy modelling methods to inform aquaculture site selection for novel seaweed species," Ecological Modelling, Elsevier, vol. 429(C).
    4. Kubokawa, Tatsuya & Srivastava, Muni S., 2008. "Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 1906-1928, October.
    5. Huazhen Lin & Ling Zhou & Chunhong Li & Yi Li, 2014. "Semiparametric transformation models for semicompeting survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 599-607, September.
    6. Václavík, Tomáš & Meentemeyer, Ross K., 2009. "Invasive species distribution modeling (iSDM): Are absence data and dispersal constraints needed to predict actual distributions?," Ecological Modelling, Elsevier, vol. 220(23), pages 3248-3258.
    7. Wongsathit Wongloet & Prach Kongthong & Aingorn Chaiyes & Worapong Singchat & Warong Suksavate & Nattakan Ariyaraphong & Thitipong Panthum & Artem Lisachov & Kitipong Jaisamut & Jumaporn Sonongbua & T, 2023. "Genetic Monitoring of the Last Captive Population of Greater Mouse-Deer on the Thai Mainland and Prediction of Habitat Suitability before Reintroduction," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    8. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    9. Hossain, Ahmed & Beyene, Joseph & Willan, Andrew R. & Hu, Pingzhao, 2009. "A flexible approximate likelihood ratio test for detecting differential expression in microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3685-3695, August.
    10. Luca Scrucca, 2014. "Graphical tools for model-based mixture discriminant analysis," 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. 8(2), pages 147-165, June.
    11. Inês Silva & Matthew Crane & Pongthep Suwanwaree & Colin Strine & Matt Goode, 2018. "Using dynamic Brownian Bridge Movement Models to identify home range size and movement patterns in king cobras," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-20, September.
    12. Wei Yang & Yuanxu Ma & Linhai Jing & Siyuan Wang & Zhongchang Sun & Yunwei Tang & Hui Li, 2022. "Differential Impacts of Climatic and Land Use Changes on Habitat Suitability and Protected Area Adequacy across the Asian Elephant’s Range," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    13. Gong Chen & Qing Zhou, 2010. "Heterogeneity in DNA Multiple Alignments: Modeling, Inference, and Applications in Motif Finding," Biometrics, The International Biometric Society, vol. 66(3), pages 694-704, September.
    14. Bilin Zeng & Xuerong Meggie Wen & Lixing Zhu, 2017. "A link-free sparse group variable selection method for single-index model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2388-2400, October.
    15. Haoxiang Zhao & Shanqing Yi & Yu Zhang & Nianwan Yang & Jianyang Guo & Hongmei Li & Xiaoqing Xian & Wanxue Liu, 2024. "Estimating the Optimal Control Areas of Two Classical Biocontrol Agents Against the Fall Armyworm Based on Hotspot Matching Analysis," Agriculture, MDPI, vol. 14(12), pages 1-14, December.
    16. Ramos, Rodrigo Soares & Kumar, Lalit & Shabani, Farzin & Picanço, Marcelo Coutinho, 2019. "Risk of spread of tomato yellow leaf curl virus (TYLCV) in tomato crops under various climate change scenarios," Agricultural Systems, Elsevier, vol. 173(C), pages 524-535.
    17. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    18. Liu, Canran & White, Matt & Newell, Graeme & Griffioen, Peter, 2013. "Species distribution modelling for conservation planning in Victoria, Australia," Ecological Modelling, Elsevier, vol. 249(C), pages 68-74.
    19. Won, Joong-Ho & Lim, Johan & Yu, Donghyeon & Kim, Byung Soo & Kim, Kyunga, 2014. "Monotone false discovery rate," Statistics & Probability Letters, Elsevier, vol. 87(C), pages 86-93.
    20. Jan, Budczies & Kosztyla, Daniel & von Törne, Christian & Stenzinger, Albrecht & Darb-Esfahani, Silvia & Dietel, Manfred & Denkert, Carsten, 2014. "cancerclass: An R Package for Development and Validation of Diagnostic Tests from High-Dimensional Molecular Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i01).

    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:gam:jmathe:v:9:y:2021:i:21:p:2826-:d:673830. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.