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Confidence interval estimation of partial area under curve based on combined biomarkers

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  • Tian, Lili

Abstract

In diagnostic studies, we often need to combine several markers to increase the diagnostic accuracy. This paper addresses the problem of confidence interval estimation of partial area under receiver-operating characteristic (ROC) curve for the combined marker based on the optimal linear combination proposed by Su and Liu (1993). The proposed approach is developed using the concepts of generalized inference. Numerical study demonstrates that the proposed approach generally can provide reasonable confidence intervals via a few straightforward simulation steps.

Suggested Citation

  • Tian, Lili, 2010. "Confidence interval estimation of partial area under curve based on combined biomarkers," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 466-472, February.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:2:p:466-472
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    References listed on IDEAS

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    1. Tian, Lili & Wilding, Gregory E., 2008. "Confidence interval estimation of a common correlation coefficient," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4872-4877, June.
    2. K. Krishnamoorthy & Yong Lu, 2003. "Inferences on the Common Mean of Several Normal Populations Based on the Generalized Variable Method," Biometrics, The International Biometric Society, vol. 59(2), pages 237-247, June.
    3. Hari K. Iyer & C.M. Jack Wang & Thomas Mathew, 2004. "Models and Confidence Intervals for True Values in Interlaboratory Trials," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 1060-1071, December.
    4. Samaradasa Weerahandi & Vance W. Berger, 1999. "Exact Inference for Growth Curves with Intraclass Correlation Structure," Biometrics, The International Biometric Society, vol. 55(3), pages 921-924, September.
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    Cited by:

    1. Yousef, Waleed A., 2013. "Assessing classifiers in terms of the partial area under the ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 64(C), pages 51-70.
    2. 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.
    3. Man-Jen Hsu & Huey-Miin Hsueh, 2013. "The linear combinations of biomarkers which maximize the partial area under the ROC curves," Computational Statistics, Springer, vol. 28(2), pages 647-666, April.

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