assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity … and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a …

, cases and controls, and is com- monly summarized by using the receiver operating characteristic (ROC) curve. In studies of … classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three ways of … adjustment. For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a …

to compare the sensitivity, specificity and area under the ROC curve (AUC) of indices constructed using equal number of …, specificity and AUC of an index increase as the number of partitions of components increases. However, the rate that the … regression model for each component was proposed as potential weight. The current simulation results showed that the sensitivity …

The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the … outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC … curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do …

by the model, or R^2, commonly used for continuous response models, or using extensions of sensitivity and specificity … on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to … Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time …