extract_all_tree_cuts

Consolidates cut information from all fitted policy trees. This is the
default behavior that returns cuts from all trees regardless of which
tree identified the selected subgroup.

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_all_tree_cuts function

<dl><dt>trees</dt>
<dd>List. Policy trees (indexed by depth)</dd>
<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth</dd></dl>

Arguments

Extract all cuts from fitted trees — extract_all_tree_cuts

<dl>

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


<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth</dd>

</dl>

extract_all_tree_cuts: Extract all cuts from fitted trees

Description

Usage

Value

Arguments