plot_sg_forest

Creates a forest plot showing hazard ratios with confidence intervals.

internal

Implements statistical methods for exploratory subgroup identification
in clinical trials with survival endpoints. Provides tools for identifying
patient subgroups with differential treatment effects using machine learning
approaches including Generalized Random Forests (GRF), LASSO regularization,
and exhaustive combinatorial search algorithms. Features bootstrap bias
correction using infinitesimal jackknife methods to address selection bias
in post-hoc analyses. Designed for clinical researchers conducting exploratory
subgroup analyses in randomized controlled trials, particularly for
multi-regional clinical trials (MRCT) requiring regional consistency
evaluation. Supports both accelerated failure time (AFT) and Cox proportional
hazards models with comprehensive diagnostic and visualization tools.
Methods are described in León et al. (2024) <doi:10.1002/sim.10163>.

Larry Leon

forestsearch

Exploratory Subgroup Identification in Clinical Trials with
Survival Endpoints

plot_sg_forest function

<dl><dt>hr_estimates</dt>
<dd>Data frame with HR estimates</dd>
<dt>sg0_name</dt>
<dd>Character. Label for H subgroup</dd>
<dt>sg1_name</dt>
<dd>Character. Label for Hc subgroup</dd>
<dt>colors</dt>
<dd>List. Color specifications</dd>
<dt>title</dt>
<dd>Character. Plot title</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Plot Forest Plot of Hazard Ratios — plot_sg_forest

<dl>

<dt>hr_estimates</dt>
<dd>Data frame with HR estimates</dd>


<dt>sg0_name</dt>
<dd>Character. Label for H subgroup</dd>


<dt>sg1_name</dt>
<dd>Character. Label for Hc subgroup</dd>


<dt>colors</dt>
<dd>List. Color specifications</dd>


<dt>title</dt>
<dd>Character. Plot title</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

plot_sg_forest: Plot Forest Plot of Hazard Ratios

Description

Usage

Value

Arguments