intPredErrCompRisks: Integrated Prediction Error for Competing Risks

In discSurv: Discrete Time Survival Analysis

Integrated Prediction Error for Competing Risks

Description

Estimates integrated prediction errors of arbitrary prediction models in the case of competing risks.

Usage

intPredErrCompRisks(
  testPreds,
  testDataShort,
  trainDataShort,
  timeColumn,
  eventColumns,
  tmax = NULL
)

Arguments

testPreds	Predictions on the test data with model fitted on training data. Must be a matrix, with the predictions in the rows and the number of columns equal to the number of events.
testDataShort	Test data in short format.
trainDataShort	Train data in short format.
timeColumn	Character giving the column name of the observed times.
eventColumns	Character vector giving the column names of the event indicators (excluding censoring column).
tmax	Gives the maximum time interval for which prediction errors are calculated. It must not be higher than the maximum observed time in the training data.

Value

Integrated prediction errors for each competing event. Matrix, with the integrated predictions in the rows and the number of columns equal to the number of events.

Author(s)

Moritz Berger [e-mail]
https://www.imbie.uni-bonn.de/personen/dr-moritz-berger/

References

\insertRef

heyardValCompRisksdiscSurv

See Also

predErrCompRisks, predErrCurve

Examples

###########################
# Example unemployment data
library(Ecdat)
data(UnempDur)

# Select subsample
selectInd1 <- 1:200
selectInd2 <- 201:400
trainSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd1], ]
testSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd2], ]

# Convert to long format 
trainSet_long <- dataLongCompRisks(dataShort=trainSet, timeColumn="spell", 
eventColumns=c("censor1", "censor4"), timeAsFactor=TRUE)
tmax          <- max(trainSet$spell)
testSet_long <- dataLongCompRisks(dataShort=testSet, timeColumn="spell", 
eventColumns=c("censor1", "censor4"), aggTimeFormat = TRUE, lastTheoInt=tmax,
timeAsFactor=TRUE)

# Estimate continuation ratio model with logit link
vglmFit <- VGAM::vglm(formula=cbind(e0, e1, e2) ~ timeInt + age + logwage, 
data=trainSet_long, family=VGAM::multinomial(refLevel="e0"))

# Calculate predicted hazards
predHazards <- VGAM::predictvglm(vglmFit, newdata=testSet_long, type="response")

# Compute integrated prediction error 
intPredErrCompRisks(testPreds=predHazards[,-1], testSet, trainSet, "spell", 
c("censor1", "censor4"), tmax)

discSurv documentation built on March 18, 2022, 7:12 p.m.