Experimental Cox-Time Neural Survival Learner

Fits a neural Cox-Time model allowing time-dependent covariate effects through survivalmodels::coxtime(). This optional adapter requires Python torch, torchtuples, and pycox and supports only uniform observation weights.

Usage

surv.coxtime(
  time,
  event,
  X,
  newdata = NULL,
  new.times,
  obsWeights = NULL,
  id = NULL,
  num_nodes = c(32L, 32L),
  activation = "relu",
  batch_norm = TRUE,
  dropout = NULL,
  epochs = 100L,
  batch_size = 128L,
  device = NULL,
  verbose = FALSE,
  seed = 1L,
  standardize_time = TRUE,
  ...
)

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.
num_nodes: Positive integers giving hidden-layer sizes.
activation: Neural-network activation name.
batch_norm: Whether to use batch normalization.
dropout: Optional dropout probability in [0, 1).
epochs: Number of training epochs.
batch_size: Training and prediction batch size.
device: Optional device passed to survivalmodels.
verbose: Whether the Python backend should print training progress.
seed: Positive integer used for Python, NumPy, and torch random-number generators.
standardize_time: Whether to standardize outcome times using the training-only Cox-Time label transformation. Predictions are returned on the original time scale.
...: Additional named fitting arguments passed to survivalmodels::coxtime(), such as frac for an internal training-fold validation split or early_stopping. Outcomes and features are managed by the adapter.

Value

A list with numeric survival matrix pred and fitted object fit.

Details

Native survival curves are evaluated as right-continuous steps on the backend's time grid, using survival one before the first grid point and the final available value beyond the last point. This is not a claim of reliable extrapolation beyond observed follow-up. Python-backed fitted objects are intended for reuse within the active R/Python session; plain saveRDS() is not a portable persistence format for Python objects.

Examples

if (interactive() && requireNamespace("survivalmodels", quietly = TRUE) &&
    requireNamespace("reticulate", quietly = TRUE) &&
    reticulate::py_module_available("pycox")) {
  data("metabric", package = "SuperSurv")
  dat <- metabric[1:60, ]
  X <- dat[, "x1", drop = FALSE]
  fit <- surv.coxtime(dat$duration, dat$event, X,
                      X[1:3, , drop = FALSE], c(50, 100),
                      epochs = 2, batch_norm = FALSE, device = "cpu")
  dim(fit$pred)
}