Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package

Sample size and statistical power are important factors to consider when planning
a research synthesis. Power analysis methods have been developed for
fixed effect or random effects models, but until recently these methods were
limited to simple data structures with a single, independent effect per study.
Recent work has provided power approximation formulas for meta-analyses
involving studies with multiple, dependent effect size estimates, which are
common in syntheses of social science research. Prior work focused on developing
and validating the approximations but did not address the practice challenges
encountered in applying them for purposes of planning a synthesis
involving dependent effect sizes. We aim to facilitate the application of these
recent developments by providing practical guidance on how to conduct power
analysis for planning a meta-analysis of dependent effect sizes and by introducing
a new R package, POMADE, designed for this purpose. We present a comprehensive
overview of resources for finding information about the study
design features and model parameters needed to conduct power analysis, along
with detailed worked examples using the POMADE package. For presenting
power analysis findings, we emphasize graphical tools that can depict power
under a range of plausible assumptions and introduce a novel plot, the traffic
light power plot, for conveying the degree of certainty in one's assumptions.