Wrapper for BART (Bayesian Additive Regression Trees)

Final Production Wrapper for BART (Tunable & Robust). Uses the mc.surv.bart function. Automatically reshapes the flat output vector into a survival matrix and interpolates the predictions to the requested new.times.

Usage

surv.bart(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  ntree = 10,
  ndpost = 30,
  nskip = 10,
  ...
)

Arguments

time: Observed follow-up time.
event: Observed event indicator.
X: Training covariate data.frame.
newdata: Test covariate data.frame to use for prediction.
new.times: Times at which to obtain predicted survivals.
obsWeights: Observation weights (Note: BART does not natively support weights).
id: Optional cluster/individual ID indicator.
ntree: Number of trees (default: 50).
ndpost: Number of posterior draws (default: 1000).
nskip: Number of burn-in draws (default: 250).
...: Additional arguments passed to mc.surv.bart.

Value

A list containing:

fit: The fitted model object (e.g., the raw coxph or xgb.Booster object). If the model fails to fit, this may be an object of class try-error.
pred: A numeric matrix of cross-validated survival predictions evaluated at the specified new.times grid.

Examples

if (.Platform$OS.type != "windows" &&
  requireNamespace("BART", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:20, ]
  x_cols <- grep("^x", names(dat))[1:3]
  X <- dat[, x_cols, drop = FALSE]
  newX <- X[1:5, , drop = FALSE]
  times <- seq(50, 150, by = 50)

  fit <- surv.bart(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    ntree = 3,
    ndpost = 5,
    nskip = 5
  )

  dim(fit$pred)
}