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metapoweR

CRAN status Lifecycle: stable

The primary goal of metapower is to compute statistical power for meta-analyses. Currently, metapower has the following functionality:

Computation of statistical power for:

  1. Summary main effects sizes
  2. Test of homogeneity for between-group variance (for Random-effects models).
  3. Test of homogeneity for within-study variance
  4. Subgroup Analyses
  5. Moderator Analysis

metapower can currently handle the following designs and effect sizes:

  1. Standardized mean difference: Cohen’s d
  2. Correlation between two continuous variables: Correlation Coefficient (via Fisher’s r-to-z transformation)
  3. Probability of Success/Failure: Odds Ratio

Installation

You can install the released version of metapower from CRAN with:

install.packages("metapower")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jasonwgriffin/metapower")

Shiny Application

Check out the simple and easy to use shiny application

Example

library(metapower)
my_power <- mpower(effect_size = .3, study_size = 20, k = 10, i2 = .50, es_type = "d")
print(my_power)
#> 
#>  Power Analysis for Meta-analysis 
#> 
#>  Effect Size Metric:                d 
#>  Expected Effect Size:              0.3 
#>  Expected Study Size:               20 
#>  Expected Number of Studies:        10 
#> 
#>  Estimated Power: Mean Effect Size 
#> 
#>  Fixed-Effects Model                0.5594533 
#>  Random-Effects Model (i2 = 50%):   0.3454424
plot_mpower(my_power)

See Vignette “Using metapower” for more information..

References

All mathematical calculations are derived from Hedges & Pigott (2004), Bornstein, Hedges, Higgins, & Rothstein (2009),Pigott (2012), Jackson & Turner (2017).

Bornstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta-analysis. Hoboken, NJ: Wiley.

Hedges, L. V., & Pigott, T. D. (2004). The power of statistical tests for moderators in meta-analysis. Psychological Methods, 9(4), 426–445. https://doi.org/10.1037/1082-989x.9.4.426

Jackson, D., & Turner, R. (2017). Power analysis for random‐effects meta-analysis. Research Synthesis Methods, 8(3), 290–302. https://doi.org/10.1002/jrsm.1240

Pigott, T. D. (2012). Advances in meta-analysis. NewYork, NY: Springer.

Issues

If you encounter a clear bug, please file a minimal reproducible example on github.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.