timeROC (version 0.4)

plotAUCcurve: Plot time-dependent AUC curve

Description

This function plots the curve of time-dependent-AUC: AUC(t) versus t. Pointwise and simultaneous confidence bands for this curve can also be plotted when inverse probability of censoring weights are computed from a Kaplan-Meier estimator.

Usage

plotAUCcurve(object, FP = 2, add = FALSE, conf.int = FALSE,
             conf.band = FALSE, col = "black")

Arguments

object

An object of class "ipcwsurvivalROC" or "ipcwcompetingrisksROC" previously estimated from the timeROC function.

FP

In the competing risks setting, a numeric value that indicates which definition of AUC is plotted. 1 for definition (i) and 2 for definition (ii). (See details of timeROC function for definitions (i) and (ii)). Default is FP = 2.

add

A logical value that indicates if you want to add the AUC curve to a pre-existing plot. Default is add = FALSE.

conf.int

A logical value that indicates whether or not you want to plot the bands of pointwise confidence intervals. Default is conf.int = FALSE. For choosing conf.int = TRUE, the object object must have been estimated by the timeROC function with arguments weighting="marginal" and iid = TRUE.

conf.band

A logical value that indicates whether or not you want to plot the simultaneous confidence bands. Default is conf.band = FALSE. For choosing conf.band = TRUE, the object object must have been estimated by the timeROC function with arguments weighting="marginal" and iid = TRUE.

col

The color to plot the AUC curve. Default is col = "black".

References

Blanche, P., Dartigues, J. F., & Jacqmin-Gadda, H. (2013). Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in medicine, 32(30), 5381-5397.

Hung, H. and Chiang, C. (2010). Estimation methods for time-dependent AUC with survival data. Canadian Journal of Statistics, 38(1):8-26

See Also

• confint for confidence intervals and confidence bands computation of time-dependentAUC.

• plotAUCcurveDiff for plotting the curve of the difference of two time-dependent AUCs over time with eventually confidence intervals and simultaneous confidence bands.

Examples

# NOT RUN {
# }
# NOT RUN {
## computation times is roughly 10 seconds

##-------------Without competing risks-------------------
library(survival)
data(pbc)
head(pbc)
pbc<-pbc[!is.na(pbc$trt),] # select only randomised subjects
pbc$status<-as.numeric(pbc$status==2) # create event indicator: 1 for death, 0 for censored     
# we evaluate bilirubin as a prognostic biomarker for death.
ROC.bili<-timeROC(T=pbc$time,
                  delta=pbc$status,marker=pbc$bili,
                  cause=1,weighting="marginal",
                  times=quantile(pbc$time,probs=seq(0.2,0.8,0.02)),
                  iid=TRUE)
ROC.bili    
# we evaluate bilirubin as a prognostic biomarker for death.
ROC.albumin<-timeROC(T=pbc$time,
                     delta=pbc$status,marker=-pbc$albumin,
                     cause=1,weighting="marginal",
                     times=quantile(pbc$time,probs=seq(0.2,0.8,0.02)),
                     iid=TRUE)
ROC.albumin
# plot AUC curve for albumin only with pointwise confidence intervals
# and simultaneous confidence bands
plotAUCcurve(ROC.albumin,conf.int=TRUE,conf.band=TRUE)
# plot AUC curve for albumin and bilirunbin  with pointwise confidence intervals
plotAUCcurve(ROC.albumin,conf.int=TRUE,col="red")
plotAUCcurve(ROC.bili,conf.int=TRUE,col="blue",add=TRUE)
legend("bottomright",c("albumin","bilirunbin"),col=c("red","blue"),lty=1,lwd=2)


##-------------With competing risks-------------------     
data(Melano)
head(Melano)     
# Evaluate tumor thickness as a prognostic biomarker for
# death from malignant melanoma.
ROC.thick<-timeROC(T=Melano$time,delta=Melano$status,
                   marker=Melano$thick,cause=1,
                   times=quantile(Melano$time,probs=seq(0.2,0.8,0.01)),
                   iid=TRUE)
ROC.thick
# plot AUC curve for tumor thickness with pointwise confidence intervals
# and simultaneous confidence bands
plotAUCcurve(ROC.thick,FP=2,conf.int=TRUE,conf.band=TRUE)
# }