get_cox_partial_likelihood

This function calculates the partial likelihood for the Cox proportional hazards model. The partial likelihood is computed for the censored observations in the dataset.

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_partial_likelihood 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>status</dt>
<dd>A vector indicating the event status (1 for event occurred, 0 for censored).</dd>
<dt>coefs</dt>
<dd>A vector of regression coefficients.</dd>
<dt>entry_points</dt>
<dd>A vector of entry points (optional). Defaults to NULL, in which case a vector of zeros is used.</dd></dl>

Arguments

Compute the Partial Likelihood for the Cox Proportional Hazards Model — get_cox_partial_likelihood

<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>status</dt>
<dd>A vector indicating the event status (1 for event occurred, 0 for censored).</dd>


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


<dt>entry_points</dt>
<dd>A vector of entry points (optional). Defaults to NULL, in which case a vector of zeros is used.</dd>

</dl>

get_cox_partial_likelihood: Compute the Partial Likelihood for the Cox Proportional Hazards Model

Description

Usage

Value

Arguments