@load_dependent @rombarthelemy @kennethbaillie which applies to frequentist and Bayesian methods alike.

@load_dependent @rombarthelemy @kennethbaillie the more I look into this area, the more I see that there are political or historical reasons under pinning the way we do things, rather than scientific. For example, many simple tests used today are in use because they have quite elegant analytic solutions. But that just isn’t necessary anymore when you can use things like MCMC and prioritise using a model that is suited to the situation.

@load_dependent @rombarthelemy @kennethbaillie yes, that’s certainly important. I would like to see more decision theory incorporated into the planning of clinical trials. I tend to have a lower alpha requirement for mortality end points than other non-patient centered outcomes. And to be fair to Fisher, he was quite against the Neyman-Pearson approach to “error” and promoted the use of p values as continuous measures of evidence.

@kennethbaillie @load_dependent looking at those in the room, this does appear to be a stellar nexus of intensive care meets biostats. I’m keen 😂

@rombarthelemy sorry if the discourse has reminded you of Twitter. Apologies if my rebuttal has been too robust.

@load_dependent a lot of the Rstat community seem to have moved away. The echo chamber has gotten a lot worse. It’s quite hard to break out of UK centric issues (the algo is super pushy). And while they are important issues, I do like to talk about other things from time to time!!

@rombarthelemy @kennethbaillie in fact, from a pure frequentist standpoint, the probability that the confidence intervals contains the true effect is either zero or one (it either does not, or does). We just don’t know which, so we control the long term error rates so that we are more often correct in our assertions. Using those rules of frequentism, I will assert that there is an effect here. And I am likely to be correct in the long term at an error rate that I am comfortable with.