Individual participant data versus aggregate data

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.

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.

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.

Worth separating two things that post #20 runs together.

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.

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.