schemper.Err: Distance-based estimator of survival predictive accuracy...

Description Usage Arguments Details Value References See Also Examples

Description

Schemper and Henderson's estimator of the absolute deviation between survival functions

Usage

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schemper(train.fit, traindata, newdata)

Arguments

train.fit

A cph object containing the fit of a Cox proportional hazards model.

traindata

A data frame containing the set of training data.

newdata

A data frame containing the set of test data.

Details

This code has been adapted from Lusa et al. (2007). Schemper and Henderson's estimator (as implemented by Lusa et al. 2007) assumes that predictions of the time-to-event outcome were obtained from a Cox proportional hazards model. The estimator is valid as long as the Cox model is specified correctly.

Technical details:

For details on the estimator and its implementation, we refer to Schemper and Henderson (2000) and Lusa et al. (2007).

Value

schemper returns a list with the following components:

Model

The call to cph.

D

The estimator of predictive accuracy obtained from the covariate-free null model.

Dx

The estimator of predictive accuracy obtained from the Cox model.

V

The estimator of relative gains in predictive accuracy.

Mhat

The absolute distance estimator obtained from the Cox model (evaluated at the event times of the test data).

Mhat.0

The absolute distance estimator obtained from the covariate-free null model (evaluated at the event times of the test data).

timep

The event times of the test data.

References

Schemper, M. and R. Henderson (2000).
Predictive accuracy and explained variation in Cox regression.
Biometrics 56, 249–255.

Lusa, L., R. Miceli and L. Mariani (2007).
Estimation of predictive accuracy in survival analysis using R and S-PLUS.
Computer Methods and Programms in Biomedicine 87, 132–137.

See Also

IntAUC, predErr, OXS

Examples

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library(rms)
ovarian$time <- ovarian$futime
ovarian$status <- ovarian$fustat
set.seed(2011)
trobs <- sample(1:26,16)
TR <- ovarian[trobs,]
TE <- ovarian[-trobs,]
train.fit  <- cph(Surv(time, status) ~ age,
                  x=TRUE, y=TRUE, method="breslow", data=TR)

schemper(train.fit, TR, TE)

survAUC documentation built on May 2, 2019, 3:23 a.m.