get_best_survreg

Extracts the best fitting model object from a comparison result.
If no single best model can be determined, returns the Weibull model if
selected by either AIC or BIC. Defaults to Weibull0 model if no model
can be determined.

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

get_best_survreg function

<dl><dt>comparison_result</dt>
<dd>Output from compare_survreg_models or compare_multiple_survreg</dd></dl>

Arguments

Get Best Model from Comparison — get_best_survreg

<dl>

<dt>comparison_result</dt>
<dd>Output from compare_survreg_models or compare_multiple_survreg</dd>

</dl>

get_best_survreg: Get Best Model from Comparison

Description

Usage

Value

Arguments