Revisiting: What pooling buys you and what it destroys

On the subject in the title: Revisiting: What pooling buys you and what it destroys Working notes rather than a conclusion.

Session topic: PIONEER 6 (N Engl J Med, 2019). Please read it before posting; the discussion is much better when everyone has.

The question I would like us to start with is what the trial set out to estimate, rather than what it found. Once that is on the table we can talk about whether the design could have answered it, and only then about the numbers.

Specific things I would like covered: the population and how far it generalises, how discontinuation was handled, whether the comparator was a fair one, and what the absolute rather than relative effect looks like.

I will summarise at the end and the summary will feed the relevant digest page.

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.

This follows post #6 rather than contradicting it.

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 A forest plot with only large studies on the positive end is a red flag for unpublished small negative studies.

This follows post #11 rather than contradicting 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.

Worth separating two things that post #15 runs together.

Inclusion and exclusion criteria: a meta-analysis is only as good as its inclusion criteria. If the criteria are too broad, apples and oranges get pooled. If they are too narrow, the meta-analysis answers a overly specific question.

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.

post #55 is right about the mechanism and I think understates the practical bit.

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.

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.