Deep Neural Networks for Survival Analysis Using torch
survdnn implements neural network-based models for right-censored
survival analysis using the native torch backend in R. It supports
multiple loss functions including Cox partial likelihood, L2-penalized
Cox, Accelerated Failure Time (AFT) objectives, as well as
time-dependent extension such as Cox-Time. The package provides a
formula interface, supports model evaluation using time-dependent
metrics (e.g., C-index, Brier score, IBS), cross-validation, and
hyperparameter tuning.
- Formula interface for
Surv() ~ .models - Modular neural architectures: configurable layers, activations, and losses
- Built-in survival loss functions:
"cox": Cox partial likelihood"cox_l2": penalized Cox"aft": Accelerated Failure Time"coxtime": deep time-dependent Cox (like DeepSurv)
- Evaluation: C-index, Brier score, Integrated Brier Score (IBS)
- Model selection with
cv_survdnn()andtune_survdnn() - Prediction of survival curves via
predict()andplot()
# Install from CRAN
install.packages("surdnn")
# Install from GitHub
install.packages("remotes")
remotes::install_github("ielbadisy/survdnn")
# Or clone and install locally
git clone https://github.com/ielbadisy/survdnn.git
setwd("survdnn")
devtools::install()library(survdnn)
library(survival, quietly = TRUE)
library(ggplot2)
veteran <- survival::veteran
mod <- survdnn(
Surv(time, status) ~ age + karno + celltype,
data = veteran,
hidden = c(32, 16),
epochs = 100,
loss = "cox",
verbose = TRUE
)## Epoch 50 - Loss: 3.933090
## Epoch 100 - Loss: 3.858984
summary(mod)##
## ── Summary of survdnn model ──────────────────────────────────────────────────────────────────────────
##
## Formula:
## Surv(time, status) ~ age + karno + celltype
## <environment: 0x57d73a0ad3e0>
##
## Model architecture:
## Hidden layers: 32 : 16
## Activation: relu
## Dropout: 0.3
## Final loss: 3.858984
##
## Training summary:
## Epochs: 100
## Learning rate: 1e-04
## Loss function: cox
##
## Data summary:
## Observations: 137
## Predictors: age, karno, celltypesmallcell, celltypeadeno, celltypelarge
## Time range: [ 1, 999 ]
## Event rate: 93.4%
plot(mod, group_by = "celltype", times = 1:300)# Cox partial likelihood
mod1 <- survdnn(
Surv(time, status) ~ age + karno,
data = veteran,
loss = "cox",
epochs = 100
)## Epoch 50 - Loss: 3.919740
## Epoch 100 - Loss: 3.919716
# Accelerated Failure Time
mod2 <- survdnn(
Surv(time, status) ~ age + karno,
data = veteran,
loss = "aft",
epochs = 100
)## Epoch 50 - Loss: 17.547979
## Epoch 100 - Loss: 17.413593
# Deep time-dependent Cox (Coxtime)
mod3 <- survdnn(
Surv(time, status) ~ age + karno,
data = veteran,
loss = "coxtime",
epochs = 100
)## Epoch 50 - Loss: 4.953788
## Epoch 100 - Loss: 4.839940
cv_results <- cv_survdnn(
Surv(time, status) ~ age + karno + celltype,
data = veteran,
times = c(30, 90, 180),
metrics = c("cindex", "ibs"),
folds = 3,
hidden = c(16, 8),
loss = "cox",
epochs = 100
)
print(cv_results)grid <- list(
hidden = list(c(16), c(32, 16)),
lr = c(1e-3),
activation = c("relu"),
epochs = c(100, 300),
loss = c("cox", "aft", "coxtime")
)
tune_res <- tune_survdnn(
formula = Surv(time, status) ~ age + karno + celltype,
data = veteran,
times = c(90, 300),
metrics = "cindex",
param_grid = grid,
folds = 3,
refit = FALSE,
return = "summary"
)
print(tune_res)plot(mod1, group_by = "celltype", times = 1:300)
plot(mod1, group_by = "celltype", times = 1:300, plot_mean_only = TRUE)
help(package = "survdnn")
?survdnn
?tune_survdnn
?cv_survdnn
?plot.survdnn# Run all tests
devtools::test()The survdnn R package is available on CRAN or at:
https://github.com/ielbadisy/survdnn
Contributions, issues, and feature requests are welcome. Open an issue or submit a pull request!
MIT © Imad El Badisy