Wrapper for Generalized Additive Cox Regression (GAM)

Final Production Wrapper for GAM (Tunable & Robust). Uses gam to fit an additive combination of smooth and linear functions.

Usage

surv.gam(time, event, X, newdata, new.times, obsWeights, id, cts.num = 5, ...)

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: Ignored, as mgcv uses weights for the event indicator).
id: Optional cluster/individual ID indicator.
cts.num: Cutoff of unique values at which a numeric covariate receives a smooth term (s).
...: Additional arguments passed to gam.

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("mgcv", 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.gam(
    time = dat$duration,
    event = dat$event,
    X = X,
    newdata = newX,
    new.times = times,
    obsWeights = rep(1, nrow(dat)),
    id = NULL,
    cts.num = 5
  )

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