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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