Evidence · Meta-analyses · continued

Coming back to: Reading a meta-analysis you disagree with, fairly posts 31–35

Evidence › Meta-analyses meta-analysis publication bias heterogeneity risk of bias

This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.

j.lokkenTL2 Moderator9 Feb 2025#31

Sensitivity analysis: the authors re-run the meta-analysis excluding studies one at a time, or by quality, to see whether the pooled estimate changes. Robust results stay similar even when individual studies are excluded.

23 likes 18mo

s.stavrianosTL2Member11 Feb 2025#32

Inclusion and exclusion criteria: a meta-analysis is only as good as its inclusion criteria. If the criteria are too broad, apples and oranges get pooled. If they are too narrow, the meta-analysis answers a overly specific question.

0 likes 18mo

l.krastevTL2 Moderator13 Feb 2025#33

e.mwangi, post #27: Number needed to treat from a meta-analysis: can be computed from the pooled estimate if the baseline risk is specified. More interpretable than pooled relative effects. Go to post

I disagree with the reply above, and I think the disagreement is substantive rather than terminological.

The distinction being drawn does not survive when you look at the published data for this specific question. I would be glad to be shown wrong on this, because the version I am arguing against is more convenient.

1 like in reply to #27 17mo

DKwiatkowskiTL3Regular15 Feb 2025#34

On post #30 — agreed on the reasoning, with one qualification.

Study quality and weighting: some meta-analyses weight all studies equally; others weight by study size or study quality. The choice affects the result and should be stated and justified.

6 likes 17mo

ew.kuuselaTL2 Moderator16 Feb 2025#35

This follows post #32 rather than contradicting it.

Funnel plots: a plot of study effect size versus sample size that helps detect publication bias. If small studies are missing on the negative side, the funnel is asymmetrical.

31 likes 17mo

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