Time ROC

Authors

[Editor] Hu Zheng;

[Contributors]

Modified

2026-04-20

๐Ÿค– AI Skill โ€” Download the Bizard unified skill for your AI assistant โฌ‡๏ธ Download Skill ZIP
Note

Hiplot website

This page is the tutorial for source code version of the Hiplot Time ROC plugin. You can also use the Hiplot website to achieve no code ploting. For more information please see the following link:

https://hiplot.cn/basic/time-roc?lang=en

Receiver Operating Characteristic (ROC) analysis with time records in survival analysis.

Setup

  • System Requirements: Cross-platform (Linux/MacOS/Windows)

  • Programming language: R

  • Dependent packages: data.table; jsonlite; plotROC; survivalROC; ggplot2; grid

# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("plotROC", quietly = TRUE)) {
  install.packages("plotROC")
}
if (!requireNamespace("survivalROC", quietly = TRUE)) {
  install.packages("survivalROC")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("grid", quietly = TRUE)) {
  install.packages("grid")
}

# Load packages
library(data.table)
library(jsonlite)
library(plotROC)
library(survivalROC)
library(ggplot2)
library(grid)
sessioninfo::session_info("attached")
โ”€ Session info โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
 setting  value
 version  R version 4.5.3 (2026-03-11)
 os       Ubuntu 24.04.4 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  C.UTF-8
 ctype    C.UTF-8
 tz       UTC
 date     2026-04-21
 pandoc   3.1.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.9.37 @ /usr/local/bin/quarto

โ”€ Packages โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€
 package     * version    date (UTC) lib source
 data.table  * 1.18.2.1   2026-01-27 [1] RSPM
 ggplot2     * 4.0.2.9000 2026-04-14 [1] Github (tidyverse/ggplot2@7d79c95)
 jsonlite    * 2.0.0      2025-03-27 [1] RSPM
 plotROC     * 2.3.3      2025-08-25 [1] RSPM
 survivalROC * 1.0.3.1    2022-12-05 [1] RSPM

 [1] /home/runner/work/_temp/Library
 [2] /opt/R/4.5.3/lib/R/site-library
 [3] /opt/R/4.5.3/lib/R/library
 * โ”€โ”€ Packages attached to the search path.

โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€

Data Preparation

  • : (Numeric) survival data (i.e survive, risk).
  • : (Numeric) time data.
# Load data
data1 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/time-roc/data.json")$exampleData$textarea[[1]])
data1 <- as.data.frame(data1)
data2 <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/time-roc/data.json")$exampleData$textarea[[2]])
data2 <- as.data.frame(data2)

# convert data structure
surv_table <- data1
colnames(surv_table) <- c("surv", "cens", "risk")
mtime <- as.data.frame(data2)[, 1]
sroc <- lapply(mtime, function(t) {
  stroc <- survivalROC(
    Stime = surv_table$surv,
    status = surv_table$cens,
    marker = surv_table$risk,
    predict.time = t,
    method = "KM"
  )
  data.frame(
    TPF = stroc[["TP"]],
    FPF = stroc[["FP"]],
    cut = stroc[["cut.values"]],
    time = rep(
      stroc[["predict.time"]],
      length(stroc[["TP"]])
    ),
    AUC = rep(
      stroc$AUC,
      length(stroc$FP)
    )
  )
})
mroc <- do.call(rbind, sroc)
mroc$time <- factor(mroc$time)

# View data
head(data1)
       surv cens       risk
1 11.126027    0 0.19205450
2  9.794521    0 0.47734974
3 13.690411    0 0.04605343
4 10.068493    0 0.29717146
5  3.317808    0 0.18144610
6 12.312329    0 0.62681895
head(data2)
  times
1     2
2     4
3     6
4     8
5    10

Visualization

# Time ROC
col <- c("#E64B35FF","#4DBBD5FF","#00A087FF","#3C5488FF","#F39B7FFF")
p <- ggplot(mroc, aes(x = FPF, y = TPF, label = cut, color = time)) +
  plotROC::geom_roc(labels = FALSE, stat = "identity", n.cuts = 0) +
  geom_abline(slope = 1, intercept = 0, color = "red", linetype = 2) +
  labs(title = "ROC Dependence Time", x = "False positive rate",
       y = "True positive rate", 
       color = paste("Time", "(", "Year", ")")) +
  theme_bw() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12, hjust = 0.5),
        axis.title = element_text(size = 10),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10)) +
  scale_color_manual(values = col)

auc <- levels(factor(mroc$AUC))
for (i in 1:length(auc)) {
  p <- p + annotate("text",
    x = 0.75,
    y = 0.05 + 0.05 * i, ## ๆณจ้‡Štext็š„ไฝ็ฝฎ
    col = col[i],
    label = paste(
      paste(paste(mtime[i], "Year", sep = " "), " = "),
      round(as.numeric(auc[i]), 2)
    )
  )
}

p
Figureย 1: Time ROC