Pooling trials with different estimands — what changed since

On the opening post — agreed on the reasoning, with one qualification.

A meta-analysis of four small trials is not stronger evidence than one adequately powered trial, whatever the summary statistic looks like.

The short version is the first sentence; the rest is why.

Double extraction with disagreement resolution is standard and is worth checking for, because single extraction errors are common and non-random.

Fixed-effect and random-effects models answer different questions. The first assumes one true effect; the second assumes a distribution of them. Choosing between them is an assumption, not a technicality.

That is where I would start, not where I would stop.

Quality assessment of included trials should change the analysis rather than sit beside it. A sensitivity analysis excluding the weakest studies is the minimum.

I had written a reply contradicting post #28 and deleted it. Here is what survived.

If you are new and reading this thread for the answer to Pooling trials with different estimands: the answer is conditional, the conditions are in the third reply, and the rest of the thread is worth skipping.

Fair, and the limits you put on it are the part I will remember.

Post #37 describes the usual case. This is about the unusual one.

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.

The arithmetic in post #53 is right; the assumption feeding it is the part to check.

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.

The claim is narrower than it sounds, and deliberately so.

Taking post #63 at face value and following it one step further.

The failure mode on Pooling trials with different estimands is boring rather than dramatic. It is almost always the step everyone assumes was done correctly because it is too simple to get wrong.