ibs_survmat

Computes the Integrated Brier Score (IBS) over a set of evaluation time points,
using trapezoidal integration and IPCW adjustment for right-censoring.

Provides deep learning models for right-censored survival data using the 'torch' backend.
Supports multiple loss functions, including Cox partial likelihood, L2-penalized Cox, time-dependent Cox,
and accelerated failure time (AFT) loss. Offers a formula-based interface, built-in support for cross-validation,
hyperparameter tuning, survival curve plotting, and evaluation metrics such as the C-index, Brier score,
and integrated Brier score. For methodological details, see Kvamme et al. (2019) <https://www.jmlr.org/papers/v20/18-424.html>.

Imad El Badisy

survdnn

Deep Neural Networks for Survival Analysis Using 'torch'

Imad EL BADISY

ibs_survmat function

<dl><dt>object</dt>
<dd>A `Surv` object with observed time and status.</dd>
<dt>sp_matrix</dt>
<dd>A data frame or matrix of predicted survival probabilities.
Each column corresponds to a time point in `times`.</dd>
<dt>times</dt>
<dd>A numeric vector of time points. Must match the columns of `sp_matrix`.</dd></dl>

Arguments

Integrated Brier Score (IBS) from a Survival Probability Matrix — ibs_survmat

<dl>

<dt>object</dt>
<dd>A `Surv` object with observed time and status.</dd>


<dt>sp_matrix</dt>
<dd>A data frame or matrix of predicted survival probabilities.
Each column corresponds to a time point in `times`.</dd>


<dt>times</dt>
<dd>A numeric vector of time points. Must match the columns of `sp_matrix`.</dd>

</dl>

ibs_survmat: Integrated Brier Score (IBS) from a Survival Probability Matrix

Description

Usage

Value

Arguments

Examples