Chambless and Diao's estimator of cumulative/dynamic AUC for right-censored time-to-event data
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.- lp
The vector of predictors estimated from the training data.
- lpnew
The vector of predictors obtained from the test data.
- times
A vector of time points at which to evaluate AUC.
Value
AUC.cd returns an object of class survAUC.
Specifically, AUC.cd 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.
Details
This function implements the estimator of cumulative/dynamic AUC proposed in
Section 3.3 of Chambless and Diao (2006). In contrast to the general form of
Chambless and Diao's estimator, AUC.cd is restricted to Cox
regression. Specifically, it is assumed that lp and lpnew are
the predictors of a Cox proportional hazards model. Estimates obtained from
AUC.cd are valid as long as the Cox model is specified correctly.
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 recursive estimators proposed in Sections 3.1 and 3.2 of Chambless and Diao (2006) are not implemented in the survAUC package.
References
Chambless, L. E. and G. Diao (2006).
Estimation of time-dependent area
under the ROC curve for long-term risk prediction.
Statistics in
Medicine 25, 3474–3486.
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)
lp <- predict(train.fit)
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_CD <- AUC.cd(Surv.rsp, Surv.rsp.new, lp, lpnew, times)