AUC.hc | R Documentation |
Hung and Chiang's estimator of cumulative/dynamic AUC for right-censored time-to-event data
AUC.hc(Surv.rsp, Surv.rsp.new, lpnew, times)
Surv.rsp |
A |
Surv.rsp.new |
A |
lpnew |
The vector of predictors obtained from the test data. |
times |
A vector of time points at which to evaluate AUC. |
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).
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 |
The vector of time points at which AUC is evaluated. |
iauc |
The summary measure of AUC. |
Hung, H. and C.-T. Chiang (2010).
Estimation methods for time-dependent
AUC models with survival data.
Canadian Journal of Statistics
38, 8–26.
AUC.uno
, AUC.sh
, AUC.cd
,
IntAUC
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
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