`R/power.analysis.subgroup.R`

`power.analysis.subgroup.Rd`

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)

TE1 | 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. |
---|---|

TE2 | 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. |

seTE1 | Pooled standard error of the first subgroup of studies. Either |

seTE2 | Pooled standard error of the second subgroup of studies. Either |

sd1 | Pooled standard deviation of the first subgroup of studies. Either |

sd2 | Pooled standard deviation of the second subgroup of studies. Either |

var1 | Pooled variance of the first subgroup of studies. Either |

var2 | Pooled variance of the second subgroup of studies. Either |

two.tailed | Logical. Should a two-tailed ( |

Returns a `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`

: A`data.frame`

containing the data used to generate the plot in`Plot`

.`Test`

: The type of test used for the power calculations (`"one.tailed"`

or`"two.tailed"`

).`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%