deepSurv {dnn}

Deep learning for the Cox proportional hazards model

Description

Fit a survival regression model under the Cox proportional hazards assumption using deep learning neural network (DNN).

Usage

deepSurv(formula, model, data, epochs = 200, lr_rate = 1e-04,
        batch_size = 64, alpha = 0.7, lambda = 1, verbose = 0,
        weights = NULL, ...)

Arguments

formula

a formula expression as for other regression models. The response is usually a survival object as returned by the 'Surv' function. See the documentation for 'Surv', 'lm' and 'formula' for details.

model

a deep neural network model, created by function dNNmodel().

data

a data.frame in which to interpret the variables named in the formula.

epochs

number of deep learning epochs, default is 200.

batch_size

batch size, default is 64. 'NaN' may be generated if batch size is too small and there is not event in a batch.

lr_rate

learning rate for the gradient descent algorithm, default is lr_rate = 1e-04.

weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.

alpha

momentum rate for the gradient descent method, alpha takes value in [0, 1), default is alpha = 0.70.

lambda

L2 regularization parameter for deep learning.

verbose

verbose = 1 for print out verbose during the model fit, 0 for not print.

...

optional arguments

Details

See "Deep learning with R" for details on how to build a deep learning model.

The following parameters in 'dnnControl' will be used to control the model fit process.

'epochs': number of deep learning epochs, default is 30.

'verbose': verbose = 1 for print out verbose during the model fit, 0 for not print.

'epsilon': epsilon for convergence check, default is epsilon = 0.001.

'max.iter': number of maximum iteration, default is max.iter = 30.

When the variance for covariance matrix X is too large, please use xbar = scale(x) to standardize X.

Value

An object of class "deepSurv" is returned. The deepSurv object contains the following list components:

x

Covariates for Cox model

y

Surv object for Cox model

model

dnn model

predictor

predictor score mu = f(x)

risk

risk score = exp(predictor)

Note

For right censored survival time only

Author(s)

Chen, B. E. wrote the R code using the partial likelihood cost function proposed by Katzman et al (2018).

References

Katzman JL, Shaham U, Cloninger A, Bates J, Jiang T, Kluger Y. DeepSurv: Personalized treatment recommender system using a Cox proportional hazards deep neural network. BMC Medical Research Methodology 2018; 18: 24.

See Also

deepAFT, deepGlm, print.deepSurv, survreg

Examples

## Example for deep learning proportional hazards survival model
  set.seed(101)
### define model layers
  model = dNNmodel(units = c(4, 3, 1), activation = c("elu", "sigmoid", "sigmoid"), 
                   input_shape = 3)
  x = matrix(runif(15), nrow = 5, ncol = 3)
  time = exp(x[, 1])
  status = c(1, 0, 1, 1, 1)
  fit = deepSurv(Surv(time, status) ~ x, model = model)