survdnn

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.

Features

• 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() and tune_survdnn()
• Prediction of survival curves via predict() and plot()

Installation

# 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()

Quick Example

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: 3.987919
## Epoch 100 - Loss: 3.974391

summary(mod)

## 

## ── Summary of survdnn model ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────

## 
## Formula:
##   Surv(time, status) ~ age + karno + celltype
## <environment: 0x5b3739336aa0>
## 
## Model architecture:
##   Hidden layers:  32 : 16 
##   Activation:  relu 
##   Dropout:  0.3 
##   Final loss:  3.974391 
## 
## 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)

Loss Functions

# Cox partial likelihood
mod1 <- survdnn(
  Surv(time, status) ~ age + karno,
  data = veteran,
  loss = "cox",
  epochs = 100
  )

## Epoch 50 - Loss: 4.216911
## Epoch 100 - Loss: 4.105076

# Accelerated Failure Time
mod2 <- survdnn(
  Surv(time, status) ~ age + karno,
  data = veteran,
  loss = "aft",
  epochs = 100
  )

## Epoch 50 - Loss: 21.136486
## Epoch 100 - Loss: 20.663244

# Deep time-dependent Cox (Coxtime)
mod3 <- survdnn(
  Surv(time, status) ~ age + karno,
  data = veteran,
  loss = "coxtime",
  epochs = 100
  )

## Epoch 50 - Loss: 4.856084
## Epoch 100 - Loss: 5.289982

Cross-Validation

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)

Hyperparameter Tuning

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 Survival Curves

plot(mod1, group_by = "celltype", times = 1:300)

plot(mod1, group_by = "celltype", times = 1:300, plot_mean_only = TRUE)

Documentation

help(package = "survdnn")
?survdnn
?tune_survdnn
?cv_survdnn
?plot.survdnn

Testing

# Run all tests
devtools::test()

Availability

The survdnn R package is available at: https://github.com/ielbadisy/survdnn

The package is currently under submission to CRAN.

Contributions

Contributions, issues, and feature requests are welcome. Open an issue or submit a pull request!

License

MIT © Imad El Badisy