This function performs an a priori power estimation for a test for subgroup differences within a meta-analysis.
power.analysis.subgroup(TE1, TE2, seTE1, seTE2, sd1, sd2, var1, var2, two.tailed=TRUE)
Pooled effect size (e.g., standardized mean difference, Hedges' \(g\), log-Odds Ratio or other linear continuous effect size) of the first subgroup of studies.
Pooled effect size (e.g., standardized mean difference, Hedges' \(g\), log-Odds Ratio or other linear continuous effect size) of the second subgroup of studies.
Pooled standard error of the first subgroup of studies. Either
Pooled standard error of the second subgroup of studies. Either
Pooled standard deviation of the first subgroup of studies. Either
Pooled standard deviation of the second subgroup of studies. Either
Pooled variance of the first subgroup of studies. Either
Pooled variance of the second subgroup of studies. Either
Logical. Should a two-tailed (
list with five elements:
Power: The estimated power of the subgroup contrast, expressed as a value between 0 and 1 (i.e., 0%-100%).
Plot: A plot showing the effect size difference (x), power (y), estimated power (red point) and
estimated power for changing effect size differences (blue line). A dashed line at 80% power is also provided as a
visual threshold for sufficient power.
data.frame containing the data used to generate the plot in
Test: The type of test used for the power calculations (
Gamma: The analyzed effect size difference calculated from the inputs.
This function provides an estimate of the power \(1-\beta\) of a subgroup contrast analysis provided the assumed effect sizes in each subgroup and their dispersion measures. The function implements the formulae described by Hedges and Pigott (2001).
Hedges, L. V., & Pigott, T. D. (2001). The power of statistical tests in meta-analysis. Psychological methods, 6(3), 203.
# Example 1: using standard error and two-tailed test power.analysis.subgroup(TE1=0.30, TE2=0.66, seTE1=0.13, seTE2=0.14)#> Minimum effect size difference needed for sufficient power: 0.536 (input: 0.36) #> Power for subgroup difference test (two-tailed): 46.99%# Example 2: using variance and one-tailed test pasg = power.analysis.subgroup(TE1=-0.91, TE2=-1.22, var1 = 0.0023, var2 = 0.0078, two.tailed = FALSE) summary(pasg)#> Minimum effect size difference needed for sufficient power: 0.25 (input: 0.31) #> Power for subgroup difference test (one-tailed): 92.5%# Only show plot plot(pasg)