Accuracy of predictive ability measures for survival models

A new measure of predictive ability for survival models

In clinical research, an understanding of prognostic factors is important in the design and analysis of clinical trials and retrospective reviews of clinical experience. The results of prognostic factor studies are usually summarized in the form of statistics resulting from statistical significance testing, i.e. estimated parameters, confidence intervals, and p-values. These statistics do not i...

Measures of discrimination and predictive accuracy for interval censored survival data

Medical researchers frequently make statements that one model predicts survival better than another, and are frequently challenged to provide rigorous statistical justification for these statements. In general, it is important to quantify how well the model is able to distinguish between high risk and low risk subjects (discrimination), and how well the model predicts the probability of having ...

Predictive accuracy for chaotic economic models

In this work we present a technique to obtain prediction intervals for chaotic data. Using nearest neighbors method we give estimates of local variance and percentiles of the prediction error distribution. This allows to define an interval containing a future value with a given probability. Its effectiveness is shown with data generated by a chaotic economic model.  2001 Elsevier Science B.V. ...

Predictive ability of C-reactive protein for stroke

Survival model predictive accuracy and ROC curves.

The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R2, commonly used for continuous response models, or using extensions of sensitivity and specificity, which are commonly used for binary response models. In this article we propose new time-dependent accuracy summaries based on time-specific versions of sensit...