ROC curve and covariates: extending induced methodology to the non-parametric framework |
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Authors: | María Xosé Rodríguez-Álvarez Javier Roca-Pardiñas Carmen Cadarso-Suárez |
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Institution: | (1) Department of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, WA, USA;(2) Department of Mathematics, Departments of Urology, and Epidemiology/Biostatistics, Technical University, University of Texas Health Science Center at San Antonio, Garching, San Antonio, TX, USA; |
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Abstract: | Continuous diagnostic tests are often used to discriminate between diseased and healthy populations. The receiver operating
characteristic (ROC) curve is a widely used tool that provides a graphical visualisation of the effectiveness of such tests.
The potential performance of the tests in terms of distinguishing diseased from healthy people may be strongly influenced
by covariates, and a variety of regression methods for adjusting ROC curves has been developed. Until now, these methodologies
have assumed that covariate effects have parametric forms, but in this paper we extend the induced methodology by allowing
for arbitrary non-parametric effects of a continuous covariate. To this end, local polynomial kernel smoothers are used in
the estimation procedure. Our method allows for covariate effect not only on the mean, but also on the variance of the diagnostic
test. We also present a bootstrap-based method for testing for a significant covariate effect on the ROC curve. To illustrate
the method, endocrine data were analysed with the aim of assessing the performance of anthropometry for predicting clusters
of cardiovascular risk factors in an adult population in Galicia (NW Spain), duly adjusted for age. The proposed methodology
has proved useful for providing age-specific thresholds for anthropometric measures in the Galician community. |
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