AUC.hc {survAUC}

AUC estimator proposed by Hung and Chiang

Description

Hung and Chiang's estimator of cumulative/dynamic AUC for right-censored time-to-event data

Usage

AUC.hc(Surv.rsp, Surv.rsp.new, lpnew, times)

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.

times

A vector of time points at which to evaluate AUC.

Details

This function implements the estimator of cumulative/dynamic AUC proposed by Hung and Chiang (2010). The 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. The iauc summary measure is given by the integral of AUC on [0, max(times)] (weighted by the estimated probability density of the time-to-event outcome).

Note that the estimator implemented in AUC.hc is restricted to situations where the random censoring assumption holds (formula (4) in Hung and Chiang 2010).

Value

AUC.hc returns an object of class survAUC. Specifically, AUC.hc returns a list with the following components:

auc

The cumulative/dynamic AUC estimates (evaluated at times).

times

The vector of time points at which AUC is evaluated.

iauc

The summary measure of AUC.

References

Hung, H. and C.-T. Chiang (2010).
Estimation methods for time-dependent AUC models with survival data.
Canadian Journal of Statistics 38, 8–26.

See Also

AUC.uno, AUC.sh, AUC.cd, IntAUC

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)
times <- seq(10, 1000, 10)                  

AUC_hc <- AUC.hc(Surv.rsp, Surv.rsp.new, lpnew, times)
AUC_hc