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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

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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  • Rodríguez-Álvarez, María Xosé & Roca-Pardiñas, Javier & Cadarso-Suárez, Carmen, 2011. "A new flexible direct ROC regression model: Application to the detection of cardiovascular risk factors by anthropometric measures," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3257-3270, December.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3257-3270
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    References listed on IDEAS

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    1. GonzAlez-Manteiga, Wenceslao & Pardo-FernAndez, Juan Carlos & Van Keilegom, Ingrid, 2011. "ROC Curves in Non-Parametric Location-Scale Regression Models," LIDAM Reprints ISBA 2011010, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. 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, February.
    3. Opsomer, Jean D., 2000. "Asymptotic Properties of Backfitting Estimators," Journal of Multivariate Analysis, Elsevier, vol. 73(2), pages 166-179, May.
    4. 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.
    5. Anna N. Angelos Tosteson & Colin B. Begg, 1988. "A General Regression Methodology for ROC Curve Estimation," Medical Decision Making, , vol. 8(3), pages 204-215, August.
    6. 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.
    7. Tianxi Cai & Yingye Zheng, 2007. "Model Checking for ROC Regression Analysis," Biometrics, The International Biometric Society, vol. 63(1), pages 152-163, March.
    8. Margaret Sullivan Pepe & Tianxi Cai, 2004. "The Analysis of Placement Values for Evaluating Discriminatory Measures," Biometrics, The International Biometric Society, vol. 60(2), pages 528-535, June.
    9. Margaret Sullivan Pepe, 2000. "An Interpretation for the ROC Curve and Inference Using GLM Procedures," Biometrics, The International Biometric Society, vol. 56(2), pages 352-359, June.
    10. Yingye Zheng & Patrick Heagerty, 2004. "Semiparametric Estimation of Time-Dependent: ROC Curves for Longitudinal Marker Data," UW Biostatistics Working Paper Series 1052, Berkeley Electronic Press.
    11. 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, February.
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    Cited by:

    1. Pardo-Fernandez, Juan Carlos & Rodriguez-alvarez, Maria Xose & Van Keilegom, Ingrid, 2013. "A review on ROC curves in the presence of covariates," LIDAM Discussion Papers ISBA 2013050, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.

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