This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1 · go to the accepted answer.
post #60 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.
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.
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.
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 A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.
For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.
Coming back to post #66, because the follow-up matters more than the original answer.
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.
Fixed-effects versus random-effects models: fixed-effects assumes all studies are estimating the same thing and variation is sampling error. Random-effects assumes studies are estimating effects from different distributions and allows between-study variance. Choice matters if heterogeneity is high.
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.
Picking up post #69: that is the part I would want checked first.
Fixed-effects versus random-effects models: fixed-effects assumes all studies are estimating the same thing and variation is sampling error. Random-effects assumes studies are estimating effects from different distributions and allows between-study variance. Choice matters if heterogeneity is high.
On post #69 — agreed on the reasoning, with one qualification.
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.
For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.
Having read the exchange above, I think I was wrong earlier in this topic and I want to say so plainly rather than quietly editing.
The correction was fair and I had been repeating something I had not checked carefully enough.
This follows post #73 rather than contradicting it.
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.
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.
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 #77, because the follow-up matters more than the original answer.
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.
Picking up post #77: that is the part I would want checked first.
Thank you for the correction. I have edited my earlier post with a note rather than silently, so the thread still makes sense to read. The error was mine and it was the kind that comes from remembering a figure instead of looking it up.
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.
I read post #80 twice before replying, because I had assumed the opposite.
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 #82 is right about the mechanism and I think understates the practical bit.
For anyone arriving from a search: the marked solution above is the direct answer, and the replies underneath it add the caveats that make it safe to use.
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.
post #86 answers the question as asked. The question underneath it is different.
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.
On post #84 — agreed on the reasoning, with one qualification.
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.
Fixed-effects versus random-effects models: fixed-effects assumes all studies are estimating the same thing and variation is sampling error. Random-effects assumes studies are estimating effects from different distributions and allows between-study variance. Choice matters if heterogeneity is high.
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.