Description Usage Arguments Author(s) References See Also Examples
For Cox regression models, this function generates time-dependent ROC plot(s) (true positive rate vs false positive rate) for a given alpha
at the timepoint(s) provided based on median predicted risk.
Provided that the eNetXplorer
object was generated with survAUC=T
, the cross-validated
median AUC and 95% CI are shown in the default title.
For more details, see Heagerty et al and package survivalROC
.
1 2 3 4 | plotSurvROC(x, alpha.index=NULL, survAUC_time,
xlab="False positive rate (1 - Specificity)",
ylab="True positive rate (Sensitivity)", cex.lab=1, main=NULL, col.main="black",
cex.main=0.95, status0="censored", status1="events", ...)
|
x |
|
alpha.index |
Integer indices to select alpha values. Default is |
survAUC_time |
Timepoint(s) of interest. Must be in the same time units as the survival time
provided to build the |
xlab |
Custom x-axis label. |
ylab |
Custom y-axis label. |
cex.lab |
Axis label size. |
main |
Custom title. |
col.main |
Title color. |
cex.main |
Title size. |
status0 |
Title label for censoring ("status"=0). |
status1 |
Title label for events ("status"=1). |
... |
Additional parameters. |
Julian Candia and John S. Tsang
Maintainer: Julian Candia julian.candia@nih.gov
Blanche P, Dartigues J-F and Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks, Statistics in Medicine (2013) 32:5381-5397.
1 2 3 4 | data(breastCancerSurv)
fit = eNetXplorer(x=breastCancerSurv$predictor, y=breastCancerSurv$response, family="cox",
n_run=25, n_perm_null=15, seed=111, survAUC=TRUE, survAUC_time=c(1,5)*365)
plot(x=fit, plot.type="survROC", survAUC_time=c(1,5)*365, status0="censored", status1="deaths")
|
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