R: AUC estimator proposed by Uno et al.

AUC.uno {survAUC} | R Documentation

## AUC estimator proposed by Uno et al.

### Description

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

### Usage

    AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times, savesensspec=FALSE)
    sens.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
    spec.uno(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.
`savesensspec` | A logical specifying whether sensitivities and specificities should be saved.

### Details

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.

### Value

`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`).
`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`.

### References

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.

### See Also

`AUC.cd`, `AUC.sh`, `AUC.hc`, `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_Uno <- AUC.uno(Surv.rsp, Surv.rsp.new, lpnew, times)
    names(AUC_Uno)
    AUC_Uno$iauc

[Package survAUC version 1.0-5 Index]