evaluate_model_cox

Evaluate model with cutoffs
Evaluates the model by creating exposure variables based on the cutoffs and fitting a Cox model.

Provides a recursive gradient scanning algorithm for
discretizing continuous variables in Logistic and Cox regression
models. This algorithm is especially effective in identifying
optimal cut-points for variables with U-shaped relationships to
'lnOR' (the natural logarithm of the odds ratio) or 'lnHR' (the
natural logarithm of the hazard ratio), thereby enhancing model
fit, interpretability, and predictive power. By iteratively
scanning and calculating gradient changes, the method accurately
pinpoints critical cut-points within nonlinear relationships,
transforming continuous variables into categorical ones. This
approach improves risk classification and regression analysis
performance, increasing interpretability and practical relevance
in clinical and risk management settings.

Shuo Yang

Recursive Gradient Scanning Algorithm

Yi Fei

Jinxin Zhang

evaluate_model_cox function

<dl><dt>my_data</dt>
<dd>A dataframe containing the data.</dd>
<dt>cutoffs</dt>
<dd>A list of dataframes containing the cutoffs for each variable.</dd>
<dt>formula</dt>
<dd>An object of class "formula" describing the model structure.</dd></dl>

Arguments

Evaluate model with cutoffs
Evaluates the model by creating exposure variables based on the cutoffs and fitting a Cox model. — evaluate_model_cox

<dl>

<dt>my_data</dt>
<dd>A dataframe containing the data.</dd>


<dt>cutoffs</dt>
<dd>A list of dataframes containing the cutoffs for each variable.</dd>


<dt>formula</dt>
<dd>An object of class "formula" describing the model structure.</dd>

</dl>

evaluate_model_cox: Evaluate model with cutoffs Evaluates the model by creating exposure variables based on the cutoffs and fitting a Cox model.

Description

Usage

Value

Arguments