find_covariate_any_match

Helper function to determine if any CV fold found a subgroup involving
the same covariate (not necessarily same cut).

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

find_covariate_any_match function

<dl><dt>sg_target</dt>
<dd>Character. Target subgroup definition to match.</dd>
<dt>sg1</dt>
<dd>Character vector. Subgroup 1 labels for each fold.</dd>
<dt>sg2</dt>
<dd>Character vector. Subgroup 2 labels for each fold.</dd>
<dt>confs</dt>
<dd>Character vector. Confounder names.</dd></dl>

Arguments

Find Covariate Any Match — find_covariate_any_match

<dl>

<dt>sg_target</dt>
<dd>Character. Target subgroup definition to match.</dd>


<dt>sg1</dt>
<dd>Character vector. Subgroup 1 labels for each fold.</dd>


<dt>sg2</dt>
<dd>Character vector. Subgroup 2 labels for each fold.</dd>


<dt>confs</dt>
<dd>Character vector. Confounder names.</dd>

</dl>

find_covariate_any_match: Find Covariate Any Match

Description

Usage

Value

Arguments