int_pro: Comprehensive Prognostic Modeling Pipeline

In E2E: Ensemble Learning Framework for Diagnostic and Prognostic Modeling

Comprehensive Prognostic Modeling Pipeline

Description

Executes a complete prognostic (survival) modeling workflow including single models, bagging, and stacking ensembles. Returns C-index and time-dependent AUROC metrics.

Usage

int_pro(
  ...,
  model_names = NULL,
  tune = TRUE,
  n_estimators = 10,
  seed = 123,
  time_unit = "day",
  years_to_evaluate = c(1, 3, 5)
)

Arguments

...	Data frames for survival analysis. First = training; others = test sets. Format: first column = ID, second = outcome (0/1), third = time, remaining = features.
model_names	Character vector specifying which models to use. If NULL (default), uses all registered prognostic models.
tune	Logical, enable tuning. Default TRUE.
n_estimators	Integer, bagging iterations. Default 10.
seed	Integer for reproducibility. Default 123.
time_unit	Time unit in data: "day", "month", or "year". Default "day".
years_to_evaluate	Numeric vector of years for time-dependent AUROC. Default c(1,3,5).

Value

A list with:

• all_results: All model outputs

• cindex_matrix: C-index values (models × datasets)

• avg_auroc_matrix: Average time-dependent AUROC (models × datasets)

• model_categories: Model category labels

• dataset_names: Dataset identifiers

Examples

## Not run: 
prognosis_results <- int_pro(train_pro, test_pro1, test_pro2)

## End(Not run)

E2E documentation built on March 22, 2026, 1:07 a.m.