C-statistic by Uno et al.

Censoring-adjusted C-statistic by Uno et al.

Usage

UnoC(Surv.rsp, Surv.rsp.new, lpnew, time = NULL)

Arguments

Surv.rsp: A Surv(.,.) object containing to the outcome of the training data.
Surv.rsp.new: A Surv(.,.) object containing the outcome of the test data.
lpnew: The vector of predictors obtained from the test data.
time: A positive number restricting the upper limit of the time range under consideration.

Value

The estimated C-statistic.

Details

This function implements the censoring-adjusted C-statistic proposed by Uno et al. (2011). It has the same interpretation as Harrell's C for survival data (implemented in the rcorr.cens function of the Hmisc package).

Uno's estimator is based on inverse-probability-of-censoring weights and does not assume a specific working model for deriving the predictor lpnew. It is assumed, however, that there is a one-to-one relationship between the predictor and the expected survival times conditional on the predictor. Note that the estimator implemented in UnoC is restricted to situations where the random censoring assumption holds.

References

Harrell, F. E., R. M. Califf, D. B. Pryor, K. L. Lee and R. A. Rosati (1982).
Evaluating the yield of medical tests.
Journal of the American Medical Association 247, 2543–2546.

Harrell, F. E., K. L. Lee, R. M. Califf, D. B. Pryor and R. A. Rosati (1984).
Regression modeling strategies for improved prognostic prediction.
Statistics in Medicine 3, 143–152.

Uno, H., T. Cai T, M. J. Pencina, R. B. D'Agostino and W. L. Wei (2011).
On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.
Statistics in Medicine 30, 1105–1117.

Examples


data(cancer,package="survival")
TR <- ovarian[1:16,]
TE <- ovarian[17:26,]
train.fit  <- survival::coxph(survival::Surv(futime, fustat) ~ age,
                    x=TRUE, y=TRUE, method="breslow", data=TR)

lpnew <- predict(train.fit, newdata=TE)
Surv.rsp <- survival::Surv(TR$futime, TR$fustat)
Surv.rsp.new <- survival::Surv(TE$futime, TE$fustat)              

Cstat <- UnoC(Surv.rsp, Surv.rsp.new, lpnew)
Cstat
#> [1] 0.7333333