PrognosticAUC: Calculate Time-Dependent AUC for Survival Models

In IOBR: Immune Oncology Biological Research

Calculate Time-Dependent AUC for Survival Models

Description

Evaluates prognostic ability of a survival model by calculating time-dependent AUC at the 30th and 90th percentiles of survival time. These thresholds assess short-term and long-term predictive accuracy.

Usage

PrognosticAUC(model, newx, s, acture.y)

Arguments

model	A fitted survival model object capable of generating risk scores.
newx	A matrix or data frame of new data for prediction.
s	Lambda value for prediction. Can be numeric or '"lambda.min"'/'"lambda.1se"'.
acture.y	Data frame with 'time' and 'status' columns.

Value

A data frame with AUC values at 30th ('probs.3') and 90th ('probs.9') percentiles.

Author(s)

Dongqiang Zeng

Examples

if (requireNamespace("glmnet", quietly = TRUE) &&
  requireNamespace("survival", quietly = TRUE) &&
  requireNamespace("timeROC", quietly = TRUE)) {
  library(survival)
  set.seed(123)
  x <- matrix(rnorm(100 * 5), ncol = 5)
  y <- Surv(rexp(100), rbinom(100, 1, 0.5))
  fit <- glmnet::cv.glmnet(x, y, family = "cox")
  acture_y <- data.frame(time = y[, 1], status = y[, 2])
  auc_results <- PrognosticAUC(fit, newx = x, s = "lambda.min", acture.y = acture_y)
}

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