Component-Wise Cox Boosting Learner — surv.mboost • SuperSurv

Fits a weighted component-wise Cox proportional-hazards model using mboost::glmboost() and converts its risk score to survival probabilities using SuperSurv's weighted baseline-hazard calibration.

Usage

surv.mboost(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  mstop = 100L,
  nu = 0.1,
  center = FALSE,
  ties = c("breslow", "efron"),
  survival_transform = c("exponential", "product_limit"),
  ...
)

Arguments

time: Observed follow-up time.
event: Observed event indicator.
X: Training covariate data frame.
newdata: Covariate data frame used for prediction.
new.times: Times at which survival probabilities are requested.
obsWeights: Optional non-negative observation weights.
id: Currently ignored.
mstop: Number of boosting iterations.
nu: Boosting step size.
center: Whether to center component-wise base learners.
ties: Tied-event approximation for risk-score calibration.
survival_transform: Transformation from calibrated hazard increments to survival probabilities.
...: Additional arguments passed to mboost::glmboost().

Value

A list with numeric matrix pred and fitted object fit.

Examples

if (requireNamespace("mboost", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:40, ]
  X <- dat[, grep("^x", names(dat))[1:3], drop = FALSE]
  fit <- surv.mboost(
    dat$duration, dat$event, X, X[1:4, , drop = FALSE],
    c(50, 100), mstop = 20
  )
  dim(fit$pred)
}
#> [1] 4 2