compute_cde

Computes Controlled Direct Effect as the ratio of average hazard
contributions on the natural scale:
<code>CDE(S) = mean(exp(theta_1[S])) / mean(exp(theta_0[S]))</code>.

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

compute_cde function

<dl><dt>df</dt>
<dd>Data frame with <code>theta_0</code> and <code>theta_1</code> columns.</dd>
<dt>subset_indicator</dt>
<dd>Optional logical/integer vector for subsetting.
If provided, only rows where <code>subset_indicator == 1</code> are used.</dd></dl>

Arguments

Compute CDE from theta_0 and theta_1 — compute_cde

<dl>

<dt>df</dt>
<dd>Data frame with <code>theta_0</code> and <code>theta_1</code> columns.</dd>


<dt>subset_indicator</dt>
<dd>Optional logical/integer vector for subsetting.
If provided, only rows where <code>subset_indicator == 1</code> are used.</dd>

</dl>

compute_cde: Compute CDE from theta_0 and theta_1

Description

Usage

Value

Arguments