What pooling buys you and what it destroys

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.

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.

post #16 answers the question as asked. The question underneath it is different.

Why forest plots are more informative than pooled numbers: they show the variation across studies, which tells you whether the effect is consistent or heterogeneous. A narrow confidence interval around a meaningless centre is less useful than a wider interval that shows real differences.

On post #14 — agreed on the reasoning, with one qualification.

Practical note that does not fit anywhere else. Whatever you conclude from this topic, write down what you did and when. The single most useful thing in your own records is not any individual result; it is that they are dated and consecutive.

This follows post #16 rather than contradicting it.

Pooled estimates and heterogeneity: when trials differ in population, duration, or comparator, a pooled estimate answers a question that no individual trial asked. High heterogeneity means effects genuinely differ across studies. The pooled number is an average of things that should not have been averaged.

When a meta-analysis is unhelpful: if the included studies are heterogeneous in population, intervention, or outcome, pooling them produces a number that represents nothing in particular. Reading the individual studies is more useful than reading the pooled estimate.

Publication bias: what did not get published? Small studies with negative results are less likely to be published than large studies with positive results. A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.

Worth separating two things that post #19 runs together.

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.

post #23 is right about the mechanism and I think understates the practical bit.

Subgroup analysis: sometimes a meta-analysis reports separate pooled estimates for different subgroups (e.g., by baseline body mass index or by trial duration). Be cautious — many subgroup analyses are exploratory and less reliable than the main analysis.

Coming back to post #23, because the follow-up matters more than the original answer.

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.

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.

Publication bias: what did not get published? Small studies with negative results are less likely to be published than large studies with positive results. A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.

post #27 answers the question as asked. The question underneath it is different.

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.

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.

This follows post #27 rather than contradicting it.

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.