Evaluate survival predictions across models and times

Computes a common set of numerical benchmark results for a fitted SuperSurv object or supported standalone learner. The censoring distribution used by the Brier score and time-dependent AUC is estimated marginally by Kaplan-Meier. For conditional censoring models, resampling, inference, or formal model comparisons, use a specialist evaluator such as riskRegression::Score().

Usage

eval_benchmark(
  object,
  newdata,
  time,
  event,
  eval_times,
  risk_time = stats::median(eval_times),
  verbose = FALSE
)

Arguments

object: A fitted SuperSurv object.
newdata: A data.frame of test covariates.
time: Numeric vector of observed follow-up times for the test set.
event: Numeric vector of event indicators for the test set.
eval_times: Numeric vector of times at which to evaluate survival predictions.
risk_time: Numeric. The specific time horizon used when extracting risk scores for Uno C-index. Defaults to the median of eval_times.
verbose: Logical; if TRUE, progress messages are shown.

Value

A list of class "SuperSurv_benchmark" containing summary, a model-level table; by_time, a time-specific table; and the prediction grid and risk horizon.