R: Distance-based estimators of survival predictive accuracy

predErr {survAUC} | R Documentation

## Distance-based estimators of survival predictive accuracy

### Description

Inverse-probability-of-censoring weighted estimators of absolute and squared deviations between survival functions

### Usage

    predErr(Surv.rsp, Surv.rsp.new, lp, lpnew, times,
            type = "brier", int.type = "unweighted")

### 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 the prediction error curve.
`type` | A string specifying the type of prediction error curve: 'brier' refers to the squared deviation between predicted and observed survival (Brier score), 'robust' refers to the absolute deviation between predicted and observed survival.
`int.type` | A string specifying the type of integration method for the prediction error curves. Either 'unweighted' or 'weighted'.

### Details

This function implements two types of prediction error curves for right-censored time-to-event data: The Brier Score (`type = "brier"`, Gerds and Schumacher 2006) estimates the squared deviation between predicted and observed survival whereas the method proposed by Schmid et al. (2011) estimates the absolute deviation between predicted and observed survival (`type = "robust"`).

Both methods are based on inverse-probability-of-censoring weights and do not assume a specific working model for survival prediction. Note, however, that the estimators implemented in `predErr`, are restricted to situations where the random censoring assumption holds.

Time-independent summary measures of prediction error are given by the the areas under the prediction error curves. If `int.type = "weighted"`, prediction errors are weighted by the estimated probability density of the time-to-event outcome.

### Value

`predErr` returns an object of class `survErr`. Specifically, `predErr` returns a list containing the following components:

`error` | The prediction error estimates (evaluated at `times`).
`times` | The vector of time points at which prediction errors are evaluated.
`ierror` | The integrated prediction error.

### References

Gerds, T. A. and M. Schumacher (2006).
Consistent estimation of the expected Brier score in general survival models with right-censored event times.
Biometrical Journal 48, 1029–1040.

Schmid, M., T. Hielscher, T. Augustin, and O. Gefeller (2011).
A robust alter- native to the Schemper-Henderson estimator of prediction error.
Biometrics 67, 524–535.

### See Also

`IntAUC`, `OXS`, `schemper`

### 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 <- 1:500

    predErr(Surv.rsp, Surv.rsp.new, lp, lpnew, times,
            type = "brier", int.type = "unweighted")

    predErr(Surv.rsp, Surv.rsp.new, lp, lpnew, times,
            type = "robust", int.type = "unweighted")

    predErr(Surv.rsp, Surv.rsp.new, lp, lpnew, times,
            type = "brier", int.type = "weighted")

[Package survAUC version 1.0-5 Index]