Train a Stepwise Cox Proportional Hazards Model

Trains a Cox proportional hazards model and performs backward stepwise selection using MASS::stepAIC to select important features.

Usage

stepcox_pro(X, y_surv, tune = FALSE)

Arguments

X

A data frame of features.

y_surv

A survival::Surv object representing the survival outcome.

tune

Logical, whether to perform hyperparameter tuning (currently ignored).

Value

A list of class "train" containing the trained coxph model object after stepwise selection, names of features used in training, and model type. The returned object also includes fitted_scores (linear predictor) and y_surv.

Examples

set.seed(42)
n_samples <- 50
n_features <- 5
X_data <- as.data.frame(matrix(rnorm(n_samples * n_features), ncol = n_features))
Y_surv_obj <- survival::Surv(
  time = runif(n_samples, 100, 1000),
  event = sample(0:1, n_samples, replace = TRUE)
)

# Train the model
stepcox_model <- stepcox_pro(X_data, Y_surv_obj)
print(stepcox_model$finalModel)
#> Call:  survival::coxph(formula = y_surv_ ~ 1, data = data_for_cox)
#> 
#> Null model
#>   log likelihood= -70.22957 
#>   n= 50