fe_ma

Function that conducts fixed-effect meta-analysis

Provides meta-analysis methods that correct for
publication bias and outcome reporting bias. Four methods and a visual tool
are currently included in the package. The p-uniform method as described in
van Assen, van Aert, and Wicherts (2015) <doi:10.1037/met0000025>
can be used for estimating the average effect size, testing the null hypothesis
of no effect, and testing for publication bias using only the statistically
significant effect sizes of primary studies. The second method in the package
is the p-uniform* method as described in van Aert and van Assen (2023)
<doi:10.31222/osf.io/zqjr9>. This method is an extension of the p-uniform
method that allows for estimation of the average effect size and the
between-study variance in a meta-analysis, and uses both the statistically
significant and nonsignificant effect sizes. The third method in the package
is the hybrid method as described in van Aert and van Assen (2018)
<doi:10.3758/s13428-017-0967-6>. The hybrid method is a meta-analysis method
for combining a conventional study and replication/preregistered study while
taking into account statistical significance of the conventional study. This
method was extended in van Aert (2023) such that it allows for the inclusion
of multiple conventional and replication/preregistered studies. The p-uniform
and hybrid method are based on the statistical theory that the distribution
of p-values is uniform conditional on the population effect size. The fourth
method in the package is the Snapshot Bayesian Hybrid Meta-Analysis Method as
described in van Aert and van Assen (2018) <doi:10.1371/journal.pone.0175302>.
This method computes posterior probabilities for four true effect sizes (no,
small, medium, and large) based on an original study and replication while
taking into account publication bias in the original study. The method can
also be used for computing the required sample size of the replication akin
to power analysis in null-hypothesis significance testing. The meta-plot is
a visual tool for meta-analysis that provides information on the primary
studies in the meta-analysis, the results of the meta-analysis, and
characteristics of the research on the effect under study (van Assen et al., 2023).
Helper functions to apply the Correcting for Outcome Reporting Bias (CORB)
method to correct for outcome reporting bias in a meta-analysis (van Aert &
Wicherts, 2023).

Robbie van Aert

puniform

Meta-Analysis Methods Correcting for Publication Bias

fe_ma function

<dl><dt>yi</dt>
<dd>A vector of standardized effect sizes</dd>
<dt>vi</dt>
<dd>A vector of sampling variances belonging to the standardized effect sizes (<code>yi</code>)</dd></dl>

Arguments

fe_ma — fe_ma

<dl>

<dt>yi</dt>
<dd>A vector of standardized effect sizes</dd>


<dt>vi</dt>
<dd>A vector of sampling variances belonging to the standardized effect sizes (<code>yi</code>)</dd>

</dl>

fe_ma: fe_ma

Description

Usage

Value

Arguments

Details