CalculateTimeROC: Calculate Time-Dependent ROC Curve

In IOBR: Immune Oncology Biological Research

Calculate Time-Dependent ROC Curve

Description

Computes time-dependent ROC curve for survival models using the 'timeROC' package. Evaluates predictive accuracy at a specified time quantile.

Usage

CalculateTimeROC(model, newx, s, acture.y, modelname, time_prob = 0.9)

Arguments

model	A fitted survival model object.
newx	A matrix or data frame of new data for prediction.
s	Lambda value for prediction.
acture.y	Data frame with 'time' and 'status' columns.
modelname	Character string for model identification.
time_prob	Numeric quantile for ROC calculation. Default is '0.9'.

Value

An object of class 'timeROC' containing ROC curve information.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  dat <- na.omit(lung[, c("time", "status", "age", "sex", "ph.ecog")])
  dat$status <- dat$status - 1
  x <- as.matrix(dat[, c("age", "sex", "ph.ecog")])
  y <- Surv(dat$time, dat$status)
  fit <- glmnet::glmnet(x, y, family = "cox")
  actual_outcome <- data.frame(time = dat$time, status = dat$status)
  roc_info <- CalculateTimeROC(
    model = fit, newx = x, s = 0.01, acture.y = actual_outcome,
    modelname = "glmnet Cox Model", time_prob = 0.5
  )
  print(roc_info$AUC)
}

IOBR documentation built on May 30, 2026, 5:07 p.m.