This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
I read post #28 twice before replying, because I had assumed the opposite.
Where the pooled result and the largest single trial disagree, that disagreement is the interesting thing rather than an inconvenience to be smoothed.
The rule of thumb is fine; the edge cases are where it earns its keep.
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 would treat the number as indicative rather than as a measurement.
Two claims get bundled together under heterogeneity and they need separating. The descriptive one — this is what was observed — is usually well supported. The causal one — this is why — usually is not.
Almost every disagreement in threads like this one dissolves once you say which of the two you are making.
On heterogeneity, the part that usually goes wrong is that the question is asked as though it has one answer. It has a range, and the width of the range is the interesting bit.
If you can post the two or three numbers you are working from, several people here will check the arithmetic rather than argue about the conclusion.
Coming back to post #31, because the follow-up matters more than the original answer.
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 #34 is right about the mechanism and I think understates the practical bit.
The most useful thing anyone has posted about heterogeneity in this category was a table of what had been measured and by whom. That is what I would want again.
A note on scope: what I am saying about heterogeneity applies to the case in the first post and I would not extend it further without checking.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
I would put this at better than even and not much better.
I think the heterogeneity question is answerable and has not been answered, which is a more optimistic position than most of this thread.
Heterogeneity looks different depending on whether you are reading the primary literature or the summaries of it, and the difference is not in our favour.
Individual participant data pooling is a much stronger design than aggregate pooling and is rare because it requires cooperation rather than a search.
I am confident about the direction and much less about the magnitude.
This follows post #40 rather than contradicting it.
The practical version of heterogeneity is three sentences long. The rigorous version is three pages and reaches the same conclusion with the conditions attached.
I would keep heterogeneity and the decision it usually gets used for separate in this thread. They are related and they are not the same question, and merging them is why the last one went badly.
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 have no interest in any supplier named above.
Picking up post #44: that is the part I would want checked first.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
No disagreement from me. Posting only so the question does not look ignored.
The arithmetic in post #47 is right; the assumption feeding it is the part to check.
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.
High heterogeneity is the finding rather than a nuisance to be minimised. If the effect genuinely differs across settings, an average of those settings is an average of things that should not have been averaged.
That is my reading. Someone else read the same page differently and was reasonable.
I have three months of notes on heterogeneity and the honest summary is that the trend is real and the week-to-week numbers are noise. I nearly drew the opposite conclusion from the first fortnight.
Post #53 is the version of this I will quote in future. One addition.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
The I-squared statistic describes the proportion of variability not attributable to chance and is frequently read as a threshold. It is a description rather than a test.
This is where my knowledge stops and I would rather mark the edge than blur it.
Confirming post #56 from a second method, which matters more than confirming it from a second person.
The claim about heterogeneity upthread is stronger than its source supports. I have read the source. The source says "associated with" and the post says "causes".
My position on heterogeneity is current rather than settled. I have revised it once already and I expect to again, so treat it accordingly.
Post #58 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.