plot_tdROC_cr {tdROC}

Plot the time-dependent ROC curve with competing risk

Description

This function reads in object returned by tdROC.cr() and plot ROC curve for it.

Usage

plot_tdROC_cr(
  x,
  lwd = 2,
  xlab = "1-specificity",
  ylab = "sensitivity",
  xlim = c(0, 1),
  ylim = c(0, 1),
  col = c("red", "blue"),
  main = "ROC curve",
  abline = T,
  ...
)

Arguments

x

the object returned by tdROC.cr().

lwd

user-specified line width. Default is 2.

xlab

user-specified label for x-axis. Default is "1-specificity".

ylab

user-specified label for y-axis. Default is "sensitivity".

xlim

user-specified limit for x axis. Default is c(0,1).

ylim

user-specified limit for y axis. Default is c(0,1).

col

user-specified color for ROC curve. Defualt is "c("red", "blue")" for the primary event and competing event.

main

user-specified title for the plot. Default is "ROC curve"

abline

user-specified reference diagnol line. Default is True.

...

for future methods

Value

Returns several plots of ROC curve. For competing risk data, there are two definitions of controls introduced by Zheng et al, which was listed below

\text{Definition A:} \text{Case} k:T \le \tau, \delta = k; \text{Control}_A: (T>\tau)\cup (T \le \tau \cap \delta \ne k)

\text{Definition B:} \text{Case} k:T \le \tau, \delta = k; \text{Control}_B: (T>\tau)

For more details about above two definitions, please read details of function tdROC.cr. If the tdROC.cr object comes without bootstrap result, the ROC curve for above two definitions will be plotted together and indicated by the specified col. If the tdROC.cr object with bootstrap result, one more ROC curve with confidence interval will be plotted for each definition.

References

Zheng Y, Cai T, Jin Y, Feng Z. Evaluating prognostic accuracy of biomarkers under competing risk. Biometrics. 2012;68(2):388-396. doi:10.1111/j.1541-0420.2011.01671.x

Examples

library(survival)
data(Melano)
tdROC.cr_res <- tdROC.cr(
  X = Melano$thick, Y = Melano$time,
  delta = Melano$status, tau = 1800, nboot = 10
)
plot_tdROC_cr(tdROC.cr_res)