deepsurv: DeepSurv Survival Neural Network

Description

DeepSurv neural fits a neural network based on the partial likelihood from a Cox PH.

Usage

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

Value

An object inheriting from class deepsurv.

An object of class survivalmodel.

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.
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.CoxPH, specifies device to compute models on.
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.CoxPH.fit, elements in each batch.
epochs: (integer(1))
Passed to pycox.models.CoxPH.fit, number of epochs.
verbose: (logical(1))
Passed to pycox.models.CoxPH.fit, should information be displayed during fitting.
num_workers: (integer(1))
Passed to pycox.models.CoxPH.fit, number of workers used in the dataloader.
shuffle: (logical(1))
Passed to pycox.models.CoxPH.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.CoxPH.

References

Katzman, J. L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., & Kluger, Y. (2018). DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology, 18(1), 24. https://doi.org/10.1186/s12874-018-0482-1