extract_selected_tree_cuts

Extracts cut information only from the tree at the specified selected depth.
This provides a focused set of cuts from the tree that identified the
subgroup meeting the <code>dmin.grf</code> criterion, rather than cuts from all trees.

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

extract_selected_tree_cuts function

<dl><dt>trees</dt>
<dd>List. Policy trees (indexed by depth)</dd>
<dt>selected_depth</dt>
<dd>Integer. Depth of the selected tree (from best_subgroup$depth)</dd>
<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth (for populating tree-specific slots)</dd></dl>

Arguments

Extract cuts from selected tree only — extract_selected_tree_cuts

<dl>

<dt>trees</dt>
<dd>List. Policy trees (indexed by depth)</dd>


<dt>selected_depth</dt>
<dd>Integer. Depth of the selected tree (from best_subgroup$depth)</dd>


<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth (for populating tree-specific slots)</dd>

</dl>

extract_selected_tree_cuts: Extract cuts from selected tree only

Description

Usage

Value

Arguments

Details