We need to frame our research as associational otherwise we loose plausible deniability when the MDs reading it will inevitably interpret it causally....

A shameless plug of the SBC R package that tries to make the development cost of doing SBC as low as possible hyunjimoon.github.io/SBC/

No worries, just thought the recommendations in the paper look a bit too bleak. Also, wouldn't winner’s curse be also amenable to modelling as truncation?

First, love this. Second, shouldn't there be a modelling solution to this that lets you keep the first study in (and handle sign alignment)? E.g. treating the first study as folded normal. Might help people who fear "loosing power" by removing the study...

As far as I'm aware, this is the first time anyone has shown that innocuous data-dependent reporting practices can bias meta-analyses (please let know if I'm wrong!), so we should be very careful interpreting big metascientific meta-analyses going forward. 9/x

Another possible reason for this could be different versions of BLAS/LAPACK across machines (was once bitten by this). sessionInfo() reports this so easy to check

IMHO the underlying problem is that the standard (simpler) formulation of multinomial logistic is sensitive to category orderings. There are ways to reformulate the model to make the ordering irrelevant. I tried to give some links to lit in an answer... discourse.mc-stan.org/t/problem-in...

Very, very, slight preference for positive.

Czechia has this.

We have weirdly benefitted from the fact that all the 2010s data science boom Medium posts littering the training corpus were so consistently terrible.

Violin plots?

Interpeting individual coefficients is definitely a problem in such models, but I think the interpretation challenge can be overcome with things like estimated marginal means/g-computation so I don't think that's necessarily an argument against complex models per se.