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 (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).