Description Usage Arguments Details Value References See Also Examples

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

1 2 3 |

`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. |

`savesensspec` |
A logical specifying whether sensitivities and specificities should be saved. |

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.

`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` |
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`

.

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.

`AUC.cd`

, `AUC.sh`

, `AUC.hc`

,
`IntAUC`

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
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_Uno <- AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
names(AUC_Uno)
AUC_Uno$iauc
``` |

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