We all love to cite meta-analyses. They’re the review studies where scientists take every single piece of research ever published on a particular question, and then calculate the overall “true” effect across all of them. Putting together all those studies is a much better way to get to the truth… isn’t it?
In this episode of The Studies Show, Tom and Stuart give a intro to meta-analysis, and then talk about several major problems with the whole idea. Is meta-analysis—relied upon for making so many important scientific decisions, and cited in so many of our previous episodes—in serious need of a rethink?
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Show notes
Slide show from the Cochrane Collaboration on the basics of meta-analysis
Description of the GRADE guidelines for assessing study quality
Below is a funnel plot, a method of testing for publication bias in meta-analysis. Source: we asked an AI to randomly generate some data and display it in a funnel plot, just for illustration. This funnel plot is relatively symmetrical and probably wouldn’t indicate much publication bias:
Criticism of funnel plots; Nature news reporting on the criticism
Stuart’s Substack article on the homeopathy meta-analysis (and the retraction note for that meta-analysis)
The PET-PEESE technique for meta-analysis; and a criticism of it
Useful paper that compares between different bias-correction methods for meta-analysis
The p-curve website, which has the paper explaining the technique and a useful app where you can do your own p-curve
Stuart’s Substack article on the meta-analysis on “nudges”
Further criticism of the nudge meta-analysis, with important points about “meaningless means” (and yet more criticism)
Credits
The Studies Show is produced by Julian Mayers at Yada Yada Productions.
Episode 31: The trouble with meta-analysis