Survival Model Predictive Accuracy and ROC Curves

Patrick Heagerty and Yingye Zheng
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Patrick Heagerty: University of Washington
Yingye Zheng: Fred Hutchinson Cancer Research Center

No 1051, UW Biostatistics Working Paper Series from Berkeley Electronic Press

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.

Keywords: Cox regression; discrimination; prediction; sensitivity; specificity (search for similar items in EconPapers)
Date: 2004-07-11
Note: oai:bepress.com:uwbiostat-1051
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