DeepHit Survival Neural Network
Description
DeepHit fits a neural network based on the PMF of a discrete Cox model. This is
the single (noncompeting) event implementation.
Usage
deephit(
formula = NULL,
data = NULL,
reverse = FALSE,
time_variable = "time",
status_variable = "status",
x = NULL,
y = NULL,
frac = 0,
cuts = 10,
cutpoints = NULL,
scheme = c("equidistant", "quantiles"),
cut_min = 0,
activation = "relu",
custom_net = NULL,
num_nodes = c(32L, 32L),
batch_norm = TRUE,
dropout = NULL,
device = NULL,
mod_alpha = 0.2,
sigma = 0.1,
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, lefthandside 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 rightcensored observations.

frac 
(numeric(1))
Fraction of data to use for validation dataset, default is 0 and therefore no separate
validation dataset.

cuts 
(integer(1))
If discretise is TRUE then determines number of cutpoints for discretisation.

cutpoints 
(numeric())
Alternative to cuts if discretise is true, provide exact cutpoints for discretisation.
cuts is ignored if cutpoints is nonNULL.

scheme 
(character(1))
Method of discretisation, either "equidistant" (default) or "quantiles" .
See reticulate::py_help(pycox$models$LogisticHazard$label_transform) for more detail.

cut_min 
(integer(1))
Starting duration for discretisation, see
reticulate::py_help(pycox$models$LogisticHazard$label_transform) for more detail.

activation 
(character(1))
See get_pycox_activation.

custom_net 
(torch.nn.modules.module.Module(1))
Optional custom network built with build_pytorch_net, otherwise default architecture used.
Note that if building a custom network the number of output channels depends on cuts or
cutpoints .

num_nodes , batch_norm , dropout 
(integer()/logical(1)/numeric(1))
See build_pytorch_net.

device 
(integer(1)character(1))
Passed to pycox.models.DeepHitSingle , specifies device to compute models on.

mod_alpha 
(numeric(1))
Weighting in (0,1) for combining likelihood (L1) and rank loss (L2). See reference and
py_help(pycox$models$DeepHitSingle) for more detail.

sigma 
(numeric(1))
From eta in rank loss (L2) of ref. See reference and
py_help(pycox$models$DeepHitSingle) for more detail.

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.DeepHitSingle.fit , elements in each batch.

epochs 
(integer(1))
Passed to pycox.models.DeepHitSingle.fit , number of epochs.

verbose 
(logical(1))
Passed to pycox.models.DeepHitSingle.fit , should information be displayed during
fitting.

num_workers 
(integer(1))
Passed to pycox.models.DeepHitSingle.fit , number of workers used in the
dataloader.

shuffle 
(logical(1))
Passed to pycox.models.DeepHitSingle.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.DeepHitSingle
.
Value
An object inheriting from class deephit
.
An object of class survivalmodel
.
References
Changhee Lee, William R Zame, Jinsung Yoon, and Mihaela van der Schaar.
Deephit: A deep learning approach to survival analysis with competing risks.
In ThirtySecond AAAI Conference on Artificial Intelligence, 2018.
http://medianetlab.ee.ucla.edu/papers/AAAI_2018_DeepHit