survAUC (version 1.3-0)

AUC.uno: AUC estimator proposed by Uno et al.

Description

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

Usage

AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times, savesensspec=FALSE)
sens.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
spec.uno(Surv.rsp.new, lpnew, times)

Value

AUC.uno returns an object of class survAUC. Specifically,

AUC.uno 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.

sens.uno and spec.uno return matrices of dimensions times x

(lpnew + 1). The elements of these matrices are the sensitivity and specificity estimates for each threshold of lpnew and for each time point specified in 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.

savesensspec

A logical specifying whether sensitivities and specificities should be saved.

Details

The sens.uno and spec.uno functions implement the estimators of time-dependent true and false positive rates proposed in Section 5.1 of Uno et al. (2007).

The AUC.uno function implements the estimator of cumulative/dynamic AUC that is based on the TPR and FPR estimators proposed by Uno et al. (2007). It is given by the area(s) under the time-dependent ROC curve(s) estimated by sens.sh and spec.sh. 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).

Uno's estimators are based on inverse-probability-of-censoring weights and do 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 estimators implemented in sens.uno, spec.uno and AUC.uno are restricted to situations where the random censoring assumption holds.

References

Uno, H., T. Cai, L. Tian, and L. J. Wei (2007).
Evaluating prediction rules for t-year survivors with censored regression models.
Journal of the American Statistical Association 102, 527--537.

See Also

AUC.cd, AUC.sh, AUC.hc, 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_Uno <- AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
names(AUC_Uno)
AUC_Uno$iauc