get_cox_gradient

This function computes the gradient for the Cox proportional hazards model. The gradient
is calculated by considering the contributions of each observation to the gradient based on the
risk set at each event time.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by 'Huang et al.' (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Dongming Huang

catalytic

Tools for Applying Catalytic Priors in Statistical Modeling

Yitong Wu

Weihao Li

Ministry of Education, Singapore 

get_cox_gradient function

<dl><dt>X</dt>
<dd>A matrix of covariates (design matrix) for the Cox model.</dd>
<dt>time</dt>
<dd>A numeric vector of event times.</dd>
<dt>status</dt>
<dd>A numeric vector of event indicators (1 for event, 0 for censored).</dd>
<dt>coefs</dt>
<dd>A numeric vector of coefficients for the Cox model.</dd>
<dt>entry_points</dt>
<dd>A numeric vector of entry times for the subjects. Defaults to 0.</dd></dl>

Arguments

Compute the Gradient for Cox Proportional Hazards Model — get_cox_gradient

<dl>

<dt>X</dt>
<dd>A matrix of covariates (design matrix) for the Cox model.</dd>


<dt>time</dt>
<dd>A numeric vector of event times.</dd>


<dt>status</dt>
<dd>A numeric vector of event indicators (1 for event, 0 for censored).</dd>


<dt>coefs</dt>
<dd>A numeric vector of coefficients for the Cox model.</dd>


<dt>entry_points</dt>
<dd>A numeric vector of entry times for the subjects. Defaults to 0.</dd>

</dl>

get_cox_gradient: Compute the Gradient for Cox Proportional Hazards Model

Description

Usage

Value

Arguments