First we& #39;ll install the package via Github and then load it.

The package contains two main features:
1. Functions to calculate the statistical power of studies in a meta-analysis

2. A function to create a Firepower plot, which visualises statistical power across meta-analyses
For first example, we& #39;re going to extract some data from this forest plot published in this meta-analysis on the impact of intranasal oxytocin on social cognition https://pubmed.ncbi.nlm.nih.gov/29032324/ ">https://pubmed.ncbi.nlm.nih.gov/29032324/...

All we need is the effect sizes and confidence interval info for each study
At a minimum, the dataset for analysis needs three columns, with the following labels:

1. "yi" for the effect size
2. "lower" for the lower CI bound
3. "upper" for the upper CI bound

You can also add a column for the study name, but this isn& #39;t strictly necessary
Assuming we& #39;ve named this dataset "dat_keech", we& #39;re going to use this in the & #39;mapower_ul& #39; function. This requires three arguments:

1. The data
2. The observed summary effect size estimate
3 The name of the meta-analysis (required for the other core function we& #39;ll get to soon)
This will give us statistical power for a range of possible "true" effect sizes. The reported summary effect size ("power_es_observed") and a range of effect sizes from 0.1 to 1 (in increments on 0.1)
In this particular field, the average effect size is around 0.2, and that& #39;s probably inflated due to publication bias. So conservatively assuming that 0.2 is the true effect size, power ranges from 7% to 30% in these studies.
There& #39;s also a function for meta-analyses that report effect sizes and standard errors. To illustrate, let& #39;s extract the data from this forest plot published in this meta-analysis
For this function, only two columns are required

1. "yi" for the effect size
2. "sei" for the standard error
Assuming we& #39;ve named this dataset "dat_ooi", we& #39;re going to use this in the & #39;mapower_se& #39; function. This requires three arguments, as before

1. The data
2. The observed summary effect size estimate
3 The name of the meta-analysis
As before, this will return the statistical power for a range of possible "true" effect sizes.
Sometimes it’s useful to calculate power for a body of meta-analyses, which might be reported in the same article or across articles. But Illustrating the power of individual studies from multiple meta-analyses can be difficult to interpret if there are many studies
An alternative is to illustrate the power per meta-analysis by calculating the median power across studies. We can illustrate this with a “Firepower” plot, which we can create using the & #39;firepower& #39; function. First, we need to prepare the data. Here, we& #39;re combining three MAs
Now we& #39;re doing to create the firepower plot using the list we just created
Here& #39;s our Firepower plot https://abs.twimg.com/emoji/v2/... draggable="false" alt="🎉" title="Partyknaller" aria-label="Emoji: Partyknaller">
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