Accommodating covariates in roc analysis

21-Oct-2016 14:46 by 4 Comments

Accommodating covariates in roc analysis - who is amelie mauresmo dating 2016

estimates the covariate-specific ROC curve in the presence of a one-dimensional continuous covariate based on the induced nonparametric ROC regression approach presented in @MX11a.

We provide theoretical arguments for the validity of the estimator and demonstrate its application to data.For confidentiality reasons, we use here a simulated data set that resemble the original data.ROC (receiver operating characteristic) curve analysis is well established for assessing how well a marker is capable of discriminating between individuals who experience disease onset and individuals who do not.Some asymptotic properties of the proposed methods are derived.Simulation studies show that the grouped variable selection is superior to separate model selections.Bootstrap confidence intervals for the regression and variance functions, as well as for several accuracy measures, are obtained by setting the argument function returns the estimated regression and variance functions in both healthy and diseased populations.

As far as accuracy measures is concerned, the function provides the estimated covariate-specific ROC curve, the associated covariate-specific AUCs (with the integral being approximated by numerical integration methods), and the covariate-adjusted ROC curve (AROC) [@Janes09]. uses cookies to improve performance by remembering your session ID when you navigate from page to page. Please set your browser to accept cookies to continue.The classical (standard) approach of ROC curve analysis considers event (disease) status and marker value for an individual as fixed over time, however in practice, both the disease status and marker value change over time.Individuals who are disease-free earlier may develop the disease later due to longer study follow-up, and also their marker value may change from baseline during follow-up.Furthermore, in the ROC regression, the accuracy of area under the curve (AUC) should be the focus instead of aiming at the consistency of model selection or the good prediction performance.