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A semiparametric separation curve approach for comparing correlated ROC data from multiple markers.

ABSTRACT: In this article we propose a separation curve method to identify the range of false positive rates for which two ROC curves differ or one ROC curve is superior to the other. Our method is based on a general multivariate ROC curve model, including interaction terms between discrete covariates and false positive rates. It is applicable with most existing ROC curve models. Furthermore, we introduce a semiparametric least squares ROC estimator and apply the estimator to the separation curve method. We derive a sandwich estimator for the covariance matrix of the semiparametric estimator. We illustrate the application of our separation curve method through two real life examples.

SUBMITTER: Tang LL

PROVIDER: S-EPMC3466820 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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A semiparametric separation curve approach for comparing correlated ROC data from multiple markers.

Tang Liansheng Larry LL Zhou Xiao-Hua XH

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20120701 3

In this article we propose a separation curve method to identify the range of false positive rates for which two ROC curves differ or one ROC curve is superior to the other. Our method is based on a general multivariate ROC curve model, including interaction terms between discrete covariates and false positive rates. It is applicable with most existing ROC curve models. Furthermore, we introduce a semiparametric least squares ROC estimator and apply the estimator to the separation curve method. ...[more]

PMID: 23074360

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