R: AUC estimator proposed by Chambless and Diao

AUC.cd {survAUC} | R Documentation

## AUC estimator proposed by Chambless and Diao

### Description

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

### Usage

    AUC.cd(Surv.rsp, Surv.rsp.new = NULL, lp, 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.
`lp` | The vector of predictors estimated from the training 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 in Section 3.3 of Chambless and Diao (2006). In contrast to the general form of Chambless and Diao's estimator, `AUC.cd` is restricted to Cox regression. Specifically, it is assumed that `lp` and `lpnew` are the predictors of a Cox proportional hazards model. Estimates obtained from `AUC.cd` are valid as long as the Cox model is specified correctly. 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 recursive estimators proposed in Sections 3.1 and 3.2 of Chambless and Diao (2006) are not implemented in the survAUC package.

### Value

`AUC.cd` returns an object of class `survAUC`. Specifically, `AUC.cd` 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

Chambless, L. E. and G. Diao (2006).
Estimation of time-dependent area under the ROC curve for long-term risk prediction.
Statistics in Medicine 25, 3474–3486.

### See Also

`AUC.uno`, `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)

    lp <- predict(train.fit)
    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_CD <- AUC.cd(Surv.rsp, Surv.rsp.new, lp, lpnew, times)

[Package survAUC version 1.0-5 Index]