crps: Summarizing prediction error curves

In pec: Prediction Error Curves for Risk Prediction Models in Survival Analysis

Summarizing prediction error curves

Description

Computes the cumulative prediction error curves, aka integrated Brier scores, in ranges of time.

Usage

crps(object, models, what, times, start)

Arguments

object	An object with estimated prediction error curves obtained with the function pec
models	Which models in object$models should be considered.
what	The name of the entry in x to be cumulated. Defauls to PredErr Other choices are AppErr, BootCvErr, Boot632, Boot632plus.
times	Time points at which the integration of the prediction error curve stops.
start	The time point at which the integration of the prediction error curve is started.

Details

The cumulative prediction error (continuous ranked probability score) is defined as the area under the prediction error curve, hence the alias name, ibs, which is short for integrated Brier score.

Value

A matrix with a column for the crps (ibs) at every requested time point and a row for each model

Author(s)

Thomas A. Gerds [e-mail]

References

E. Graf et al. (1999), Assessment and comparison of prognostic classification schemes for survival data. Statistics in Medicine, vol 18, pp= 2529–2545.

Gerds TA, Cai T & Schumacher M (2008) The performance of risk prediction models Biometrical Journal, 50(4), 457–479

See Also

pec

Examples

set.seed(18713)
library(prodlim)
library(survival)
dat=SimSurv(100)
pmodel=coxph(Surv(time,status)~X1+X2,data=dat,x=TRUE,y=TRUE)
perror=pec(list(Cox=pmodel),Hist(time,status)~1,data=dat)

## cumulative prediction error
crps(perror,times=1) # between min time and 1
## same thing:
ibs(perror,times=1) # between min time and 1
crps(perror,times=1,start=0) # between 0 and 1
crps(perror,times=seq(0,1,.2),start=0) # between 0 and seq(0,1,.2)

pec documentation built on April 11, 2023, 5:55 p.m.