Description Usage Arguments Details Value Author(s) References See Also Examples
This function computes pointwise confidence interval and simultaneous confidence bands for areas under timedependent ROC curves (timedependent AUC). Pointwise confidence intervals and simultaneous confidence bands are computed from the asymptotic normality of timedependent AUC estimators. Standard errors are estimated from the iidrepresentation of the estimator. The method is at present only implemented for inverse probability of censoring weights computed from a KaplanMeier estimator.
1 2 3 4 
object 
An object of class "ipcwsurvivalROC" or "ipcwcompetingrisksROC". The object 
parm 
Dummy argument. (Not used). 
level 
The confidence level required. Default is 
n.sim 
The number of simulations for computing simultaneous confidence bands. 
... 
Dummy argument. (Not used). 
Timedependent AUC estimators are asymptotically normally distributed. Then, confidence intervals are computed using an estimate of the variance and the quantiles of the standard normal distribution. To compute the variance estimates, the function computes the empirical variance estimates of the estimated iidrepresentations of the timedependent AUC estimators. A simulation technique is used for computing appropriate quantiles of simultaneous confidence bands.
Without competing risks, a list containing :
CI_AUC
: a matrix. Columns correspond to the lower
and the upper bounds of the pointwise confidence intervals of AUC. Rows
correspond to the time points for which timedependent AUC estimator was computed.
CB_AUC
: a matrix. Columns correspond to the lower
and the upper bounds of the simultaneous confidence band of the AUC curve. Rows
correspond to the time points for which timedependent AUC estimator was computed.
C.alpha
: a numeric value corresponding to the quantile
required for simultaneous confidence band computation (estimated by
simulations).
With competing risks, a list containing :
CI_AUC_1
: a matrix. Columns correspond to the lower
and the upper bounds of the pointwise confidence intervals of AUC
with definition (i) of controls. Rows correspond to the time
points for which timedependent AUC estimator was computed.
CB_AUC_1
: a matrix. Columns correspond to the lower
and the upper bounds of the simultaneous confidence band of the AUC curve
with definition (i) of controls. Rows correspond to the time
points for which timedependent AUC estimator was computed.
C.alpha.1
: a numeric value corresponding to the quantile
required for simultaneous confidence bands computation CB_AUC_1
(estimated by
simulations).
CI_AUC_2
: a matrix. Columns correspond to the lower
and the upper bounds of the pointwise confidence intervals of AUC
with definition (ii) of controls. Rows correspond to the time
points for which timedependent AUC estimator was computed.
CB_AUC_2
: a matrix. Columns correspond to the lower
and the upper bounds of the simultaneous confidence band of the AUC curve
with definition (ii) of controls. Rows correspond to the time
points for which timedependent AUC estimator was computed.
C.alpha.2
: a numeric value corresponding to the quantile
required for simultaneous confidence band computation CB_AUC_2
(estimated by
simulations).
For AUC definitions (i) and (ii), see details about timeROC
function.
Paul Blanche [email protected]
Hung, H. and Chiang, C. (2010). Estimation methods for timedependent AUC with survival data. Canadian Journal of Statistics, 38(1):826
Blanche, P., Dartigues, J. F., & JacqminGadda, H. (2013). Estimating and comparing timedependent areas under receiver operating characteristic curves for censored event times with competing risks. Statistics in medicine, 32(30), 53815397.
timeROC
, compare
, plotAUCcurve
, plotAUCcurveDiff
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47  ##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$time,probs=seq(0.2,0.8,0.1)),
iid=TRUE)
ROC.bili
confint(ROC.bili)
##With competing risks
data(Melano)
head(Melano)
# Evaluate tumor thickness as a prognostic biomarker for
# death from malignant melanoma.
ROC.thick<timeROC(T=Melano$time,delta=Melano$status,
marker=Melano$thick,cause=1,
times=quantile(Melano$time,probs=seq(0.2,0.8,0.2)),
iid=TRUE)
ROC.thick
confint(ROC.thick)
## Not run:
data(Paquid)
head(Paquid)
# Computation requires approximately 30 seconds
# (because iid=TRUE and n=2561 subjects)
# evaluate DDST cognitive score as a prognostic tool for
# dementia onset, accounting for death without dementia competing risk.
ROC.DSST<timeROC(T=Paquid$time,
delta=Paquid$status,marker=Paquid$DSST,
cause=1,weighting="marginal",times=c(3,5),
ROC=TRUE,iid=TRUE)
ROC.DSST
confint(ROC.DSST)
## End(Not run)

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