Figure 7

Cross-validation performance of 13-variable index. The multivariate index we developed is based upon a model that includes 13 variables (see Table 2). The discrimination ability of risk scores generated from this model was evaluated based upon cross-validation methods and time-specific ROC curve metrics (e.g., see Figure 3). In each of 10,000 simulations, risk scores were calculated as linear predictors of Cox regression models that included the 13 variables listed in Table 2, with coefficients estimated using 3687 subjects randomly assigned to the testing set in each simulation, and prediction accuracy evaluated based upon discrimination ability of risk scores with respect to 410 subjects randomly assigned to the testing set in each simulation (see Methods). In part (A), the estimated time-specific ROC curve corresponding to 10-years follow-up time is shown. The dashed line corresponds to the null expectation with an AUC value of 0.50. Part (B) plots estimated values of AUC(t) across t = 1,..., 19 years of follow-up time. Each point represents the average value of AUC(t) among 10,000 simulation trials, and error bars indicate the standard deviation of AUC(t) among simulations. The solid red line indicates the mean concordance index of 0.671 among simulations, and dashed red lines represent one standard deviation above and below the mean concordance estimate.