SurvivalBART: SurvivalBART

Description

Fits an Accelerated Failure Time (AFT) model using the classical Bayesian Additive Regression Trees (BART) prior: \(\log(Y) = f(x) + \varepsilon\).

Usage

SurvivalBART(
  time,
  status,
  X_train,
  X_test = NULL,
  timescale = "time",
  number_of_trees = 200,
  k = 2,
  N_post = 1000,
  N_burn = 1000,
  verbose = TRUE,
  ...
)

Arguments

time: Outcome vector of right-censored (non-negative) survival times.
status: Event indicator (1 = event, 0 = censored).
X_train: Design matrix for training data.
X_test: Optional test matrix. If NULL, predictions are computed at the column means of X_train.
timescale: Either "time" (log-transform internally) or "log" (already log-transformed).
number_of_trees: Number of trees in the ensemble. Default is 200.
k: Scaling constant used to calibrate the prior variance of the step heights.
N_post: Number of posterior samples to store.
N_burn: Number of burn-in iterations.
verbose: Logical; print sampling progress.
...: Additional arguments passed to ShrinkageTrees to override default hyperparameters.

Details

This function provides a survival-specific interface for classical BART under an AFT formulation for right-censored outcomes.

Structural regularisation is induced through the standard Gaussian leaf prior and tree depth prior of Chipman, George & McCulloch (2010).

Users requiring alternative shrinkage priors (e.g., Horseshoe or Dirichlet splitting priors) should use ShrinkageTrees directly.

References

Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). Bayesian Additive Regression Trees. Annals of Applied Statistics.