cIndexCompRisks: Discrete Concordance Index For Competing Risks

In discSurv: Discrete Time Survival Analysis

Discrete Concordance Index For Competing Risks

Description

Estimates the discrete concordance index in the case of competing risks.

Usage

cIndexCompRisks(markers, testTime, testEvents, trainTime, trainEvents)

Arguments

markers	Predictions on the test data with model fitted on training data ("numeric matrix"). Predictions are stored in the rows and the number of columns equal to the number of events.
testTime	New time intervals in the test data (class "integer").
testEvents	New event indicators (0 or 1) in the test data ("binary matrix"). Number of columns are equal to the number of events.
trainTime	Time intervals in the training data (class "integer").
trainEvents	Event indicators (0 or 1) in the training data ("binary matrix"). Number of columns are equal to the number of events.

Value

Value of discrete concordance index between zero and one (class "numeric").

Note

It is assumed that all time points up to the last observed interval \left[ a_{q-1}, a_q \right) are available.

Author(s)

Moritz Berger [e-mail]

References

\insertRef

heyardValCompRisksdiscSurv

See Also

cIndex

Examples

##################################################
# Example with unemployment data and prior fitting

library(Ecdat)
data(UnempDur)
summary(UnempDur$spell)
# Extract subset of data
set.seed(635)
IDsample <- sample(1:dim(UnempDur)[1], 100)
UnempDurSubset <- UnempDur [IDsample, ]
set.seed(-570)
TrainingSample <- sample(1:100, 75)
UnempDurSubsetTrain <- UnempDurSubset [TrainingSample, ]
UnempDurSubsetTest <- UnempDurSubset [-TrainingSample, ]

# Convert to long format
UnempDurSubsetTrainLong <- dataLongCompRisks(dataShort = UnempDurSubsetTrain, timeColumn = "spell", 
eventColumns = c("censor1", "censor4"), timeAsFactor = TRUE)

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

gamFitPreds <- VGAM::predictvglm(vglmFit , newdata = cbind(UnempDurSubsetTest, 
timeInt = as.factor(UnempDurSubsetTest$spell)))

# Evaluate C-Index based on short data format
cIndexCompRisks(markers = gamFitPreds, 
testTime = UnempDurSubsetTest$spell, 
testEvents = UnempDurSubsetTest[, c("censor1", "censor4")], 
trainTime = UnempDurSubsetTrain$spell, 
trainEvents = UnempDurSubsetTrain[, c("censor1", "censor4")])

discSurv documentation built on April 29, 2026, 9:07 a.m.