evaluate_cuts_once

Evaluates all cut expressions once and caches results to avoid
redundant evaluation. Much faster than evaluating repeatedly.

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

evaluate_cuts_once function

<dl><dt>confs</dt>
<dd>Character vector of cut expressions.</dd>
<dt>df</dt>
<dd>Data frame to evaluate expressions against.</dd>
<dt>details</dt>
<dd>Logical. Print details during execution.</dd></dl>

Arguments

Cache and validate cut expressions efficiently — evaluate_cuts_once

<dl>

<dt>confs</dt>
<dd>Character vector of cut expressions.</dd>


<dt>df</dt>
<dd>Data frame to evaluate expressions against.</dd>


<dt>details</dt>
<dd>Logical. Print details during execution.</dd>

</dl>

evaluate_cuts_once: Cache and validate cut expressions efficiently

Description

Usage

Value

Arguments

Details