deepsurv: DeepSurv Survival Neural Network

In RaphaelS1/survivalmodels: Models for Survival Analysis

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,
  ...
)

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.

Value

An object inheriting from class deepsurv.

An object of class survivalmodel.

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

Examples

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

  # common parameters
  deepsurv(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)
}

RaphaelS1/survivalmodels documentation built on July 5, 2024, 10:17 a.m.