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. 2011 May 10;30(10):1105-17.
doi: 10.1002/sim.4154. Epub 2011 Jan 13.

On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data

Hajime Uno  1 , Tianxi Cai, Michael J Pencina, Ralph B D'Agostino, L J Wei
Affiliations

On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data

Hajime Uno et al. Stat Med. .

Abstract

For modern evidence-based medicine, a well thought-out risk scoring system for predicting the occurrence of a clinical event plays an important role in selecting prevention and treatment strategies. Such an index system is often established based on the subject's 'baseline' genetic or clinical markers via a working parametric or semi-parametric model. To evaluate the adequacy of such a system, C-statistics are routinely used in the medical literature to quantify the capacity of the estimated risk score in discriminating among subjects with different event times. The C-statistic provides a global assessment of a fitted survival model for the continuous event time rather than focussing on the prediction of bit-year survival for a fixed time. When the event time is possibly censored, however, the population parameters corresponding to the commonly used C-statistics may depend on the study-specific censoring distribution. In this article, we present a simple C-statistic without this shortcoming. The new procedure consistently estimates a conventional concordance measure which is free of censoring. We provide a large sample approximation to the distribution of this estimator for making inferences about the concordance measure. Results from numerical studies suggest that the new procedure performs well in finite sample.

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Figures

Figure 1
Figure 1
Estimates for survival functions for CV events and censoring variables with Framingham study data.
Figure 2
Figure 2
Estimates for survival functions for death and censoring variables with breast cancer data.
See this image and copyright information in PMC

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