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A new flexible direct ROC regression model: Application to the detection of cardiovascular risk factors by anthropometric measures

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  • Rodríguez-Álvarez, María Xosé
  • Roca-Pardiñas, Javier
  • Cadarso-Suárez, Carmen
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    Abstract

    The receiver operating characteristic (ROC) curve is the most widely used measure for evaluating the accuracy of diagnostic tests in terms of differentiating between two conditions. It is known that, in certain circumstances, the characteristics of the patient or the place where the diagnostic test is performed can modify the test's accuracy. A new estimator for the conditional ROC curve, based on direct modelling, is proposed. In this approach, the effect of covariates and false positive fraction on the ROC curve is modelled non-parametrically using generalised additive models (GAM) combined with local polynomial kernel smoothers. The method allows for incorporation of more than one covariate in the regression model for the ROC curve and the possible interaction between them. The proposed model's performance is examined in an in-depth simulation study. Finally, endocrine data are analysed with the aim of assessing the performance of several anthropometric measures in predicting clusters of cardiovascular risk factors in an adult population in Galicia (NW Spain), with adjustment for age and gender.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311002118
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 55 (2011)
    Issue (Month): 12 (December)
    Pages: 3257-3270

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    Handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3257-3270

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: ROC curve Generalised additive models Bootstrap Cardiovascular risk factors Anthropometric measures;

    References

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    1. Lopez-de-Ullibarri, Ignacio & Cao, Ricardo & Cadarso-Suarez, Carmen & Lado, Maria J., 2008. "Nonparametric estimation of conditional ROC curves: Application to discrimination tasks in computerized detection of early breast cancer," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2623-2631, January.
    2. Opsomer, Jean D., 2000. "Asymptotic Properties of Backfitting Estimators," Journal of Multivariate Analysis, Elsevier, vol. 73(2), pages 166-179, May.
    3. Rodríguez-Álvarez, María Xosé & Tahoces, Pablo G. & Cadarso-Suárez, Carmen & Lado, María José, 2011. "Comparative study of ROC regression techniques--Applications for the computer-aided diagnostic system in breast cancer detection," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 888-902, January.
    4. Jens Perch Nielsen & Stefan Sperlich, 2005. "Smooth backfitting in practice," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 43-61.
    5. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114.
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