puniform: Meta-Analysis Methods Correcting for Publication Bias
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 (2025) <doi:10.1037/met0000719>
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).
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