coxtime: Cox-Time Survival Neural Network

In survivalmodels: Models for Survival Analysis

Cox-Time Survival Neural Network

Description

Cox-Time fits a neural network based on the Cox PH with time-varying effects.

Usage

coxtime(
  formula = NULL,
  data = NULL,
  reverse = FALSE,
  time_variable = "time",
  status_variable = "status",
  x = NULL,
  y = NULL,
  frac = 0,
  standardize_time = FALSE,
  log_duration = FALSE,
  with_mean = TRUE,
  with_std = TRUE,
  activation = "relu",
  num_nodes = c(32L, 32L),
  batch_norm = TRUE,
  dropout = NULL,
  device = NULL,
  shrink = 0,
  early_stopping = FALSE,
  best_weights = FALSE,
  min_delta = 0,
  patience = 10L,
  batch_size = 256L,
  epochs = 1L,
  verbose = FALSE,
  num_workers = 0L,
  shuffle = TRUE,
  ...
)

Arguments

formula	(formula(1)) Object specifying the model fit, left-hand-side of formula should describe a survival::Surv() object.
data	(data.frame(1)) Training data of data.frame like object, internally is coerced with stats::model.matrix().
reverse	(logical(1)) If TRUE fits estimator on censoring distribution, otherwise (default) survival distribution.
time_variable	(character(1)) Alternative method to call the function. Name of the 'time' variable, required if formula. or x and Y not given.
status_variable	(character(1)) Alternative method to call the function. Name of the 'status' variable, required if formula or x and Y not given.
x	(data.frame(1)) Alternative method to call the function. Required if formula, time_variable and status_variable not given. Data frame like object of features which is internally coerced with model.matrix.
y	([survival::Surv()]) Alternative method to call the function. Required if formula, time_variable and status_variable not given. Survival outcome of right-censored observations.
frac	(numeric(1)) Fraction of data to use for validation dataset, default is 0 and therefore no separate validation dataset.
standardize_time	(logical(1)) If TRUE, the time outcome is standardized.
log_duration	(logical(1)) If TRUE and standardize_time is TRUE then time variable is log transformed.
with_mean	(logical(1)) If TRUE (default) and standardize_time is TRUE then time variable is centered.
with_std	(logical(1)) If TRUE (default) and standardize_time is TRUE then time variable is scaled to unit variance.
activation	(character(1)) See get_pycox_activation.
num_nodes, batch_norm, dropout	(integer()/logical(1)/numeric(1)) See build_pytorch_net.
device	(integer(1)|character(1)) Passed to pycox.models.Coxtime, specifies device to compute models on.
shrink	(numeric(1)) Passed to pycox.models.Coxtime, shrinkage parameter for regularization.
early_stopping, best_weights, min_delta, patience	(logical(1)/logical(1)/numeric(1)/integer(1) See get_pycox_callbacks.
batch_size	(integer(1)) Passed to pycox.models.Coxtime.fit, elements in each batch.
epochs	(integer(1)) Passed to pycox.models.Coxtime.fit, number of epochs.
verbose	(logical(1)) Passed to pycox.models.Coxtime.fit, should information be displayed during fitting.
num_workers	(integer(1)) Passed to pycox.models.Coxtime.fit, number of workers used in the dataloader.
shuffle	(logical(1)) Passed to pycox.models.Coxtime.fit, should order of dataset be shuffled?
...	ANY Passed to get_pycox_optim.

Details

Implemented from the pycox Python package via reticulate. Calls pycox.models.Coxtime.

Value

An object inheriting from class coxtime.

An object of class survivalmodel.

References

Kvamme, H., Borgan, Ø., & Scheel, I. (2019). Time-to-event prediction with neural networks and Cox regression. Journal of Machine Learning Research, 20(129), 1–30.

Examples

if (requireNamespaces("reticulate")) {
  # all defaults
  coxtime(data = simsurvdata(50))

  # common parameters
  coxtime(data = simsurvdata(50), frac = 0.3, activation = "relu",
    num_nodes = c(4L, 8L, 4L, 2L), dropout = 0.1, early_stopping = TRUE, epochs = 100L,
    batch_size = 32L)
}

survivalmodels documentation built on March 24, 2022, 9:05 a.m.