survdnn_losses: 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)
Value
A scalar `torch_tensor` representing the loss value.
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`).
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
Run this code# Used internally by survdnn()
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