- Survival Model Predictive Accuracy and ROC Curves
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- Abstract:
The predictive accuracy of a survival model can be summarized using extensions of the proportion of variation explained by the model, or R^2, commonly used for continuous response models, or using extensions of sensitivity and specificity which are commonly used for binary response models.
In this manuscript we propose new time-dependent accuracy summaries based on time-specific versions of sensitivity and specificity calculated over risk sets. We connect the accuracy summaries to a previously proposed global concordance measure which is a variant of Kendall's tau. In addition, we show how standard Cox regression output can be used to obtain estimates of time-dependent sensitivity and specificity, and time-dependent reciever operating characteristic (ROC) curves. Semi-parametric estimation methods appropriate for both proportional hazards and non-proportional hazards data are introduced, evaluated in simulations, and illustrated using two familiar survival data sets.
- Subject Area:
- Clinical Epidemiology, Epidemiology, Statistical Models, Survival Analysis
- Suggested Citation:
- Patrick Heagerty and Yingye Zheng,
"Survival Model Predictive Accuracy and ROC Curves"
(December 19, 2003).
UW Biostatistics Working Paper Series.
Working Paper 219.
http://www.bepress.com/uwbiostat/paper219