Wrapper function for Penalized Cox Regression (GLMNET)

Final Production Wrapper for GLMNET (Tunable & Robust). Estimates a penalized Cox model (Lasso, Ridge, or Elastic Net) with automatic lambda selection. Uses the Breslow estimator with a step-function approach for the baseline hazard.

Usage

surv.glmnet(
  time,
  event,
  X,
  newdata,
  new.times,
  obsWeights,
  id,
  alpha = 1,
  nfolds = 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 the predicted survivals.
obsWeights: Observation weights.
id: Optional cluster/individual ID indicator.
alpha: The elasticnet mixing parameter (0 = Ridge, 1 = Lasso). Default is 1.
nfolds: Number of folds for internal cross-validation to select lambda. Default is 10.
...: Additional arguments passed to cv.glmnet.

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 (requireNamespace("glmnet", quietly = TRUE)) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:30, ]
  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.glmnet(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    alpha = 1,
    nfolds = 3
  )

  dim(fit$pred)
}
#> [1] 5 3