Calculate Time-Dependent AUC for Survival Models

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

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)
}