Calculate Time-Dependent ROC Curve

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

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)
}
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#>       t=0     t=259 
#>        NA 0.6791684