This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
Picking up post #58: that is the part I would want checked first.
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 A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.
Coming back to post #60, 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.
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.
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.
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.
I read post #64 twice before replying, because I had assumed the opposite.
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.
post #66 is right about the mechanism and I think understates the practical bit.
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.
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.
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.
On post #67 — agreed on the reasoning, with one qualification.
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 #71 answers the question as asked. The question underneath it is different.
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.
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.
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 #71 runs together.
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.
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.
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.
post #80 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.
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.
Coming back to post #82, because the follow-up matters more than the original answer.
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.
This follows post #84 rather than contradicting it.
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.
I read post #86 twice before replying, because I had assumed the opposite.
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.