S3 Method for defining data-generating mechanisms. See <code>simulate_dgm()</code> for
usage and further details.

Implements a unified interface for benchmarking meta-analytic
publication bias correction methods through simulation studies (see
Bartoš et al., 2025, <doi:10.48550/arXiv.2510.19489>). It provides
1) predefined data-generating mechanisms from the literature, 2) functions
for running meta-analytic methods on simulated data, 3) pre-simulated
datasets and pre-computed results for reproducible benchmarks, 4) tools for
visualizing and comparing method performance.

Franti<c5><a1>ek Barto<c5><a1>

PublicationBiasBenchmark

Benchmark for Publication Bias Correction Methods

František Bartoš

Samuel Pawel

Björn S. Siepe

dgm function

<dl><dt>dgm_name</dt>
<dd>Character string specifying the DGM type</dd>
<dt>settings</dt>
<dd>List containing the required parameters for the DGM or
numeric condition_id</dd></dl>

Arguments

The returned data frame follows a standardized schema that downstream
functions rely on. Across the currently implemented DGMs, the following
columns are used:<ul>
<li><code>yi</code> (numeric): The effect size estimate.</li>
<li><code>sei</code> (numeric): Standard error of <code>yi</code>.</li>
<li><code>ni</code> (integer): Total sample size for the estimate
(e.g., sum over groups where applicable).</li>
<li><code>es_type</code> (character): Effect size type, used to disambiguate
the scale of <code>yi</code>. Currently used values are
<code>"SMD"</code> (standardized mean difference / Cohen's d),
<code>"logOR"</code> (log odds ratio), and <code>"none"</code>
(unspecified generic continuous coefficient).</li>
<li><code>study_id</code> (integer/character, optional): Identifier of the
primary study/cluster when a DGM yields multiple estimates per study
(e.g., Alinaghi2018, PRE). If absent, each row is treated as an
independent study.</li>
</ul>

Output Structure

DGM Method — dgm

<dl>

<dt>dgm_name</dt>
<dd>Character string specifying the DGM type</dd>


<dt>settings</dt>
<dd>List containing the required parameters for the DGM or
numeric condition_id</dd>

</dl>

The returned data frame follows a standardized schema that downstream
functions rely on. Across the currently implemented DGMs, the following
columns are used:<ul>
<li><code>yi</code> (numeric): The effect size estimate.</li>
<li><code>sei</code> (numeric): Standard error of <code>yi</code>.</li>
<li><code>ni</code> (integer): Total sample size for the estimate
(e.g., sum over groups where applicable).</li>
<li><code>es_type</code> (character): Effect size type, used to disambiguate
the scale of <code>yi</code>. Currently used values are
<code>"SMD"</code> (standardized mean difference / Cohen's d),
<code>"logOR"</code> (log odds ratio), and <code>"none"</code>
(unspecified generic continuous coefficient).</li>
<li><code>study_id</code> (integer/character, optional): Identifier of the
primary study/cluster when a DGM yields multiple estimates per study
(e.g., Alinaghi2018, PRE). If absent, each row is treated as an
independent study.</li>
</ul>

dgm: DGM Method

Description

Usage

Value

Arguments

Output Structure

See Also

Examples