discSurv (version 1.4.2)

concorIndex: Concordance Index

Description

Calculates the concordance index for discrete survival models (independent measure of time). This is the probability that, for a pair of randomly chosen comparable samples, the sample with the higher risk prediction will experience an event before the other sample or belongs to a higher binary class.

Usage

concorIndex(aucObj, printTimePoints=FALSE)

Arguments

aucObj

Object of class "discSurvAucUno". This object is created using the function aucUno

printTimePoints

Should messages be printed for each calculation of a discrete time interval? (logical scalar) Default is FALSE.

Value

List with objects

• Output: Concordance index (named numeric vector)

• Input: List with all input arguments (saved for reference)

Details

The algorithm extracts all necessary information of the auc object (e. g. marginal probabilities and survival functions).

References

Matthias Schmid, Gerhard Tutz and Thomas Welchowski, (2017), Discrimination Measures for Discrete Time-to-Event Predictions, Econometrics and Statistics, Elsevier, DOI: 10.1016/j.ecosta.2017.03.008

Hajime Uno and Tianxi Cai and Lu Tian and L. J. Wei, (2007), Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models, Journal of the American Statistical Association

Patrick J. Heagerty and Yingye Zheng, (2005), Survival Model Predictive Accuracy and ROC Curves, Biometrics 61, 92-105

See Also

aucUno

Examples

# NOT RUN {
# Example with cross validation and unemployment data 
library(Ecdat)
library(caret)
data(UnempDur)

# Evaluation of short term prediction for re-employed at full-time job
# Last interval q=14
# -> Set all time points with spell > 13 to time interval 13 and censored
lastObsInterval <- 13
UnempDurSubset <- UnempDur
UnempDurSubset[UnempDurSubset$spell > lastObsInterval, "censor1"] <- 0
UnempDurSubset[UnempDurSubset$spell > lastObsInterval, "spell"] <- lastObsInterval
head(UnempDurSubset)
range(UnempDurSubset$spell)

# Select cross-validation samples
set.seed(7550)
CVfolds <- createFolds (y=UnempDurSubset$spell, returnTrain=TRUE, k=2)

# Continuation ratio model formula
contModForm <- spell ~ logwage + ui + logwage*ui + age

# Estimate true positive rate of time interval 6: 
# Correspondes to three and a half month duration (each interval is of length two weeks)
tryTPR <- tprUno (timepoint=6, dataSet=UnempDurSubset, trainIndices=CVfolds, 
survModelFormula=contModForm, censModelFormula=censor1 ~ 1, 
linkFunc="logit", idColumn=NULL, timeAsFactor=FALSE)
tryTPR
plot(tryTPR)

# Estimate false positive rate of time interval 6:
tryFPR <- fprUno (timepoint=6, dataSet=UnempDurSubset, trainIndices=CVfolds,  
survModelFormula=contModForm, censModelFormula=censor1 ~ 1, 
linkFunc="logit", idColumn=NULL, timeAsFactor=FALSE)
tryFPR
plot(tryFPR)

# Estimate AUC rate of time interval 6:
tryAUC <- aucUno (tprObj=tryTPR, fprObj=tryFPR)
tryAUC
plot(tryAUC)

# }
# NOT RUN {
# Estimate global concordance index:
tryConcorIndex <- concorIndex (tryAUC, printTimePoints=TRUE)
tryConcorIndex
summary(tryConcorIndex)
# }