This is a continuation of a long topic, addressed by post number rather than by page. Start at post 1.
Clear enough that I do not think I have a follow-up, which is unusual.
Post #58 describes the usual case. This is about the unusual one.
Random versus fixed effects came up in a thread eighteen months ago and was answered well. I cannot find it, which is itself the problem, so here is the reconstruction.
Quality assessment of included trials should change the analysis rather than sit beside it. A sensitivity analysis excluding the weakest studies is the minimum.
My position on Random versus fixed effects is current rather than settled. I have revised it once already and I expect to again, so treat it accordingly.
On post #62 — agreed on the reasoning, with one qualification.
The arithmetic on Random versus fixed effects is the easy part and it is where the errors are, which is an uncomfortable combination. Show your working and someone will catch it.
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.
I would want the raw data before agreeing with my own summary of it.
Second-hand on Random versus fixed effects, so weight it accordingly — someone whose method I trust told me this and I have not verified it myself.
Where the pooled result and the largest single trial disagree, that disagreement is the interesting thing rather than an inconvenience to be smoothed.
On post #69 — agreed on the reasoning, with one qualification.
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.
That is the honest state of it as of this week.
Picking up post #69: that is the part I would want checked first.
Worth separating Random versus fixed effects as a question about the compound from Random versus fixed effects as a question about the documentation. They get answered by different people and only one of them is answerable here.
Individual participant data pooling is a much stronger design than aggregate pooling and is rare because it requires cooperation rather than a search.
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.
Adding the boring version of Random versus fixed effects, because the interesting version keeps getting posted and the boring one is usually right.
Check the ordinary explanations, in order, and stop when one of them accounts for what you are seeing. Most of the time the second one does.
Adding the measurement that post #73 says would settle it.
Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.
A single observation, in a thread that deserves better than single observations.
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.
What I would want before treating Random versus fixed effects as settled: the method, the sample, and whether anyone tried to find the opposite result. Two of the three are usually missing.
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.
Coming back to post #81, because the follow-up matters more than the original answer.
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 all I can say without guessing.
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.
Adding this to the thread rather than to the wiki, because I am not confident enough for the wiki.
Post #82 answers the question as asked. The question underneath it is different.
Where the Random versus fixed effects reasoning breaks down for me is the step from the group result to the individual case. That step is almost never argued for.
I read post #84 twice before replying, because I had assumed the opposite.
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.
I would treat the number as indicative rather than as a measurement.
No notes. Posting so the count is not one.
Narrowing post #86, because the general version has more than one answer.
Having read the whole Random versus fixed effects thread before replying: the question in the first post has not actually been answered yet, and three of us have answered a nearby one instead.
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.