# Kings of Lyon

I am in Lyon for a couple of days for our annual Bayes 20XX/Bayesian Biostatistics conference. I couldn’t be here for the whole time, courtesy of exams period, but what I did see, I really enjoyed!

There has been quite a number of good talks, including Sara on RDD (well… I probably would say that… But the talk was really good!). Two of Leo’s students presented some fascinating work on scientific reproducibility — Leo told me a while back he was starting a Center for Reproducible Research and clearly he got it going with interesting results.

I also enjoyed very much the talk given by Kelly Moran, who presented her work (which I think is part of her PhD) on verbal autopsy — not the “usual” sort of topic that we’ve historically seen at this conference, but very interesting.

Another talk that caught my attention is the one by Eric-Jan Wagenmakers, the maker of JASP. Eric-Jan’s talk was a bit broader than just showing off the software (although there was quite a lot of that…) and he discussed an interesting example of a paper recently published in the NEMJ. The paper discussed a study of Progesterone in Women with Bleeding in Early Pregnancy; this was a large trial (~4000 women) and the interesting bit was that it was strongly characterised as a negative finding, on the basis of \(p-\)value=0.08. So he went on to present a sort of Bayesian re-analysis to emphasise the point that the p-value is data-, but not context/exisisting information-dependent/driven. Apparently, the researchers knew a lot about the underlying biological mechanisms to determine that Progesterone is most likely *not* harmful (while clearly the evidence isn’t overwhelming in favour of the hypothesis of it being *massively* beneficial, basically whichever way you look at it).

Anyway, Eric-Jan used a live demo of `JASP` to showcase his point (or perhaps used the point to showcase `JASP`), which I think was kind of cool. Certainly, the software looks very professional and has quite a few interesting features. I think it’s built on some sort of interface (probably not much different than a Shiny App) and uses `R` in the background to generate (a relatively large set of) relevant analyses — including for the most part both a frequentist and a Bayesian version.

Like I said, I think this is cool and it can help people get into actually *using* Bayesian modelling. But I’m a bit cautious about fit-for-all-analyses, only-need-to-know-what-menus-to-pick-up kind of software. Of course, we’ve done things that “hide” Bayesian modelling under the hood ourselves, but in that case it was a highly specialised bit of modelling and I purposedly stayed clear of all-purpose programmes…