R: AUC estimator proposed by Hung and Chiang

AUC.hc {survAUC} | R Documentation

## 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

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

    lpnew <- predict(train.fit, newdata=TE)
    Surv.rsp <- Surv(TR$futime, TR$fustat)
    Surv.rsp.new <- Surv(TE$futime, TE$fustat)
    times <- seq(10, 1000, 10)

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

[Package survAUC version 1.0-5 Index]