fit_policy_trees

Fits policy trees of depths 1 through maxdepth and computes metrics

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

fit_policy_trees function

<dl><dt>X</dt>
<dd>Matrix. Covariate matrix</dd>
<dt>data</dt>
<dd>Data frame. Original data</dd>
<dt>dr.scores</dt>
<dd>Matrix. Doubly robust scores</dd>
<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth (1-3)</dd>
<dt>n.min</dt>
<dd>Integer. Minimum subgroup size</dd></dl>

Arguments

Fit policy trees up to specified depth — fit_policy_trees

<dl>

<dt>X</dt>
<dd>Matrix. Covariate matrix</dd>


<dt>data</dt>
<dd>Data frame. Original data</dd>


<dt>dr.scores</dt>
<dd>Matrix. Doubly robust scores</dd>


<dt>maxdepth</dt>
<dd>Integer. Maximum tree depth (1-3)</dd>


<dt>n.min</dt>
<dd>Integer. Minimum subgroup size</dd>

</dl>

fit_policy_trees: Fit policy trees up to specified depth

Description

Usage

Value

Arguments