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 = .2, sample_size = 20, k = 10, es_type = "d")
print(my_power)
#> 
#>  Power Analysis for Meta-analysis 
#> 
#>  Expected Effect Size:              0.2 
#>  Expected Sample Size (per group):  10 
#>  Expected Number of Studies:        10 
#> 
#>  Estimated Power: Mean Effect Size 
#> 
#>  Fixed-Effects Model                0.2917841 
#>  Random-Effects Model (i2 =  0% ):  0.2478379 
#>  Random-Effects Model (i2 = 25% ):  0.2328637 
#>  Random-Effects Model (i2 = 50% ):  0.1982394 
#>  Random-Effects Model (i2 = 75% ):  0.1446749
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.