@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.

@DocEd @rombarthelemy @kennethbaillie I think the larger problem is following habit rather than thinking through the actual question and the best way to answer it. Always using the same arbitrary alpha, always applying two-sided point null hypothesis tests etc are more at fault than frequentism itself. If bayesianism takes over but with the same pervasive statistical illiteracy, we’re no better off.

@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 😂

@DocEd @kennethbaillie He my have more, but this is from the admin dashboard:

@kennethbaillie do you have any stats on monthly users etc.? like what kind of engagement the server is seeing?

@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.

@rombarthelemy @kennethbaillie I would say that your statement is actually incorrect. That certainly is not the most probable interpretation. This is why frequentism is so difficult to interpret. Frequentism is about long term error control, so making probabilistic statements at the trial level (like you’ve just done) is fraught with difficulty.

@rombarthelemy @kennethbaillie could I recommend this book if this is something that interests you. One of the best things I’ve read on this subject.

@rombarthelemy @kennethbaillie I personally would have only reported the adjusted analysis. I think being this purist to frequentist analysis is problematic. If you are this pure, then you need to also correct for family wise error for all previous studies. Silly in the context of a totally meaningless and arbitrary threshold. 0.05 isn’t magic.

@rombarthelemy @kennethbaillie important to remember that frequentist methods were developed for agriculture. They are surprisingly ill suited to interpretation of clinical trials. But we have to work with what we have.

@rombarthelemy @kennethbaillie politely disagree. The primary analysis used pre-defined adjustment criteria to increase precision and landed with p 0.04. So in fact, the conclusion is an undersell. Of course the alpha threshold is arbitrary.

@rombarthelemy @kennethbaillie where’s the spin?

@kennethbaillie @rombarthelemy this was for A2B, but also interested to hear the spin!

@DocEd well I think it’s fair to say this community led effort was quite idealistic and we haven’t recruited enough extroverts to get it going! But #CCR24 has reminded me how much great discussion we can have inside this community without needing the bigger audience that twitter offers

I haven’t used Mastodon for a while. I’ve definitely missed how the Twitter scientific community has started to move away. How is this community going? Any tips or tricks?

@kennethbaillie excellent point!