Chapter 4 Meta-analysis in R

Learning outcomes

  1. Format data for meta-analyses in R.
  2. Explore the capacity of the R package metafor.
  3. Pilot statistics for two datasets.

Context

Try out these ideas in a meta-analysis. Securing derived data to replicate an existing synthesis was not common historically, but it is now becoming increasingly viable with open science influences on these contributions. How to do a meta-analysis is well described in the peer-reviewed literature (Field and Gillett 2010) and numerous books to name a few (Koricheva, Gurevitch, and Mengersen 2013). Doing meta-analyses using the R programming language is similarly well articulated - particularly from the documentation associated with the package metafor (Viechtbauer 2017). There are no bad choices in R with well over 100 packages associated with and supporting meta-analyses conservatively listed at 151 packages. The output of Stata (an application specific to many medical meta-analyses), the R package meta, and metafor were virtually identical in several test cases (C. J. Lortie and Filazzola 2020). Ten criteria are proposed in contrasting R packages for this task specifically, but at the current time, metafor is the most commonly used and extensively documented. Hence, consider tackling the challenges here with this package as a robust starting point and entry in meta-statistics.

The 5 primary steps for meta-analyses in R.

  1. Secure primary data.
  2. Build conceptual model for factors, reponses, and moderators.
  3. Calculate effect size(s).
  4. Fit appropriate meta-model.
  5. Explore significance levels, heterogeneity, and bias.

Products

  1. Two R scripts for meta-analysis.
  2. A sense of data structures needed for meta-analyses in the R package metafor.

Reflection questions

  1. Did the analytical process differ significantly from primary-data workflows?
  2. Do meta-models in R sufficiently report outcomes to assess strength of evidence?
  3. What other steps would be an appropriate addendum to this process?
  4. What relational or qualitative elements might be worthwhile to consider adding to meta-analyses and their interpretation for stronger evidence framing and reuse?