survdnn_losses: Loss Functions for survdnn Models

In survdnn: Deep Neural Networks for Survival Analysis Using 'torch'

Loss Functions for survdnn Models

Description

These functions define various loss functions used internally by 'survdnn()' for training deep neural networks on right-censored survival data.

Usage

cox_loss(pred, true)

cox_l2_loss(pred, true, lambda = 1e-04)

aft_loss(pred, true)

coxtime_loss(pred, true)

Arguments

pred	A tensor of predicted values (typically linear predictors or log-times).
true	A tensor with two columns: observed time and status (1 = event, 0 = censored).
lambda	Regularization parameter for 'cox_l2_loss' (default: '1e-4').

Value

A scalar 'torch_tensor' representing the loss value.

Supported Losses

- **Cox partial likelihood loss** ('cox_loss'): Negative partial log-likelihood used in proportional hazards modeling. - **L2-penalized Cox loss** ('cox_l2_loss'): Adds L2 regularization to the Cox loss. - **Accelerated Failure Time (AFT) loss** ('aft_loss'): Mean squared error between predicted and log-transformed event times, applied to uncensored observations only. - **CoxTime loss** ('coxtime_loss'): Implements the partial likelihood loss from Kvamme & Borgan (2019), used in Cox-Time models.

Examples

# Used internally by survdnn()

survdnn documentation built on Aug. 8, 2025, 6:05 p.m.