Description Usage Arguments Details Value Examples

View source: R/calculatePower.R

Calculates the statistical power of a random effects meta-analysis based on the methods described by Valentine et al. 2010, J of Educational and Behavioral Studies.

1 | ```
calcMetaPower(es, avg_n, nStudies, hg, tail=2)
``` |

`es` |
effect size you're trying to detect (e.g. 0.6) |

`avg_n` |
the average sample size of each GROUP in each STUDY (e.g. 10) |

`nStudies` |
the number of studies you put in the meta-analysis (aka Discovery cohort) (e.g. 5) |

`hg` |
heterogeneity, (".33" for small, "1" for moderate, & "3" for large) (e.g. 0.33) |

`tail` |
whether you have a one tail or two tail p-value |

Based on the paper by Valentine et al.: JC Valentine, TD Pigott, and HR Rothstein. How Many Studies Do You Need? A Primer on Statistical Power for Meta-Analysis J of Educational and Behavioral Statistics April 2010 Vol 35, No 2, pp 215-247

The code itself is adapted from a blog post by Dan Quintana, Researcher at Oslo University in Biological Psychiatry On the website Towards Data Science, July 2017

https://towardsdatascience.com/how-to-calculate-statistical-power-for-your-meta-analysis-e108ee586ae8

`avg_n`

is the average number people in each group in each study, so if you have
4 studies, and each study compared 10 cases and 10 controls, then `avg_n`

= 10.

NOTE: THIS CODE DOES NOT TAKE MULTIPLE HYPOTHESIS TESTING INTO ACCOUNT IT ASSUMES P< 0.05

For clarity, avg_n is the average number people in each group in each study, so if you have 4 studies, and each study compared 10 cases and 10 controls, then avg_n = 10.

Statistic `Power`

of the random effects meta-analysis described.
Most statisticians want a statistical power of at least 0.8, which means that there is an 80
that if there is a true effect, you will detect it.

1 2 3 4 5 | ```
# effect size =0.7
# 10 samples on average in each group in each study
# 5 studies included in meta-analysis
# low heterogeneity (0.33)
calcMetaPower(es=0.7, avg_n=10, nStudies=5, hg=0.33)
``` |

```
[1] 0.8379345
```

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