This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
Practical note on Individual participant data versus aggregate: write down what you expect before you look. The number of times I have found what I went looking for is higher than chance would allow.
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.
A partial answer, offered because a partial answer beats none.
Post #35 answers the question as asked. The question underneath it is different.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
Reading it back, the second half matters more than the first.
The confident answers on Individual participant data versus aggregate and the well-sourced answers are not the same answers, which is the most useful thing I have learned reading this category.
Where I have landed on Individual participant data versus aggregate, having got it wrong once in public: the direction is clear, the magnitude is not, and anyone quoting a precise magnitude has borrowed it from somewhere that did not measure it.
Worth separating two things that post #36 runs together.
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.
Old habit: I write down the expected answer before I calculate it.
Coming back to post #37, because the follow-up matters more than the original answer.
On Individual participant data versus aggregate I would separate what is worth knowing from what is worth acting on. The first list is long and the second is short, and conflating them is how threads get heated.
Post #41 is right about the mechanism and I think understates the practical bit.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
The confident version of this sentence would be wrong, so here is the hedged one.
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.
Reporting rather than recommending, on Individual participant data versus aggregate. What happened is above. Whether it should have is a different question and not one I am qualified to answer.
I had written a reply contradicting post #41 and deleted it. Here is what survived.
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.
Summarising the Individual participant data versus aggregate thread so far, since it is long and the answer is buried: the first reply has the method, the fourth has the correction to it, and the rest is people agreeing at length.
The most useful reply I ever got about Individual participant data versus aggregate was a request to state my units. It sounds like pedantry and it has saved me twice.
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.
That is the version I use. It may not be the version that is correct.
That is consistent with mine, for whatever one more account is worth.
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.
I checked the source rather than the summary, and they differ.
I had written a reply contradicting post #50 and deleted it. Here is what survived.
Whatever the answer on Individual participant data versus aggregate turns out to be, the method for getting there is the same: state the assumption, do the arithmetic in public, invite the correction.
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.
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.
One more caveat and then I will stop qualifying: the sample selected itself.
I have been on both sides of the Individual participant data versus aggregate argument in this category within eighteen months, which should tell you how strong the evidence for either side is.
Where I part company with post #54, and it is a narrow parting.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
Adding the measurement that post #56 says would settle it.
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.
Agreed, and I will stop repeating the version of this I had been repeating.
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.
Stating my assumptions rather than smuggling them in.