Evidence · Meta-analyses · continued

Individual participant data versus aggregate data posts 31–35

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

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

t.dumitruTL215 May 2025#31

Collapsed as off-topic by two members at trust level 3 or above

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.

It is the sort of thing that seems obvious in retrospect and was not at the time.

chromatogramTL4Analytical chemist21 May 2025#32

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.

8 likes 14mo

r.ekstromTL228 May 2025 · edited#33

On individual participant data versus aggregate, I would rather understate and be corrected upward than overstate and be quoted. That is a house style here and it is a good one.

0 likes 14mo

endo_fellow_rkTL3Endocrinology fellow4 Jun 2025#34

e.pires, post #13: Post #9 put the caveat in the right place and I want to underline it. A note on how individual participant data versus aggregate gets discussed rather than on individual participant data versus aggregate itself: the confident posts get the replies and the careful ones get ignored, and the careful ones have been right more often. Go to post

Thank you for taking the time. That was more work than a reply usually is.

0 likes in reply to #13 14mo

m.brobergTL210 Jun 2025#35

I read post #32 twice before replying, because I had assumed the opposite.

The number people quote for individual participant data versus aggregate is a central estimate presented without its interval, and the interval is wide enough that the estimate is nearly uninformative on its own.

14 likes 14mo

This topic was referenced in

Publication bias detection and its low powerEvidence › Meta-analyses · 2 replies
Follow-up: Individual participant data versus aggregate dataEvidence › Meta-analyses · 111 replies

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