Something that I keep seeing pop up is the idea that meta-analysis somehow eliminates issues with the underlying research This is just confusingly incorrect
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You can throw anything into a meta-analysis model. Here's a model I just ran on the ratio of hosting to participating in the summer Olympics. This is meaningless!pic.twitter.com/snEmahYVDg
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We tend to put meta-analyses on a pedestal, but the fact is that statistically aggregating evidence is a total waste of time if that evidence is all bad
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This recent Cochrane review is a perfect example - they looked at the evidence for ivermectin for COVID-19, but because most of it was terrible they only included a few studies in their modelhttps://twitter.com/GidMK/status/1420340231786549253?s=20 …
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This is also where the phrase "garbage in, garbage out" comes from. If your meta-analysis includes numbers from studies that are terrible, the final point estimate is as meaningless as my graph above on the Olympics
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Some people think meta-analysis is impressive because it involves fancy statistical software, but it's entirely possible to implement a Dersimonian-Laird inverse-variance model in Excel with a stats textbook and a few hours of time
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End of conversation
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Well it is not that simple. First you have the heterogeneity which is one of the many hallmarks of quality .. and then you have the authors who should comment the relevance of the findings... So at the end of the day it all comes down to who are the authors and the reviewers...
Thanks. Twitter will use this to make your timeline better. UndoUndo
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