get_cox_syn_gradient

This function calculates the gradient of the synthetic Cox proportional hazards model using a vectorized approach.

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_syn_gradient function

<dl><dt>X</dt>
<dd>A matrix of covariates with rows representing observations and columns representing features.</dd>
<dt>time</dt>
<dd>A vector of time-to-event data.</dd>
<dt>coefs</dt>
<dd>A vector of regression coefficients.</dd>
<dt>hazard_constant</dt>
<dd>A scalar representing the hazard constant.</dd></dl>

Arguments

Compute the gradient of the synthetic Cox proportional hazards model — get_cox_syn_gradient

<dl>

<dt>X</dt>
<dd>A matrix of covariates with rows representing observations and columns representing features.</dd>


<dt>time</dt>
<dd>A vector of time-to-event data.</dd>


<dt>coefs</dt>
<dd>A vector of regression coefficients.</dd>


<dt>hazard_constant</dt>
<dd>A scalar representing the hazard constant.</dd>

</dl>

get_cox_syn_gradient: Compute the gradient of the synthetic Cox proportional hazards model

Description

Usage

Value

Arguments