@kennethbaillie the editorial discussed the conf ints. effectively in terms of probability mass and gave a direct interpretation of them as if they were Bayesian. But of course a direct probability interpretation isn’t possible without casting priors.

@DocEd @kennethbaillie is it about the shameful spinning in the conclusion of BLING III?

@rombarthelemy @DocEd What was the spin?

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

@DocEd @kennethbaillie

@rombarthelemy @kennethbaillie where’s the spin?

@DocEd @kennethbaillie second sentence. It’s just the definition of the CI. It doesn’t belong to a conclusion. It is aimed at producing confusion suggesting that the results is almost positive to be in concordance with the metanalysis they also published

@DocEd @kennethbaillie they suggest that the risk of a clinical effect that is not detected by the study is high. But. This risk is known. It’s 10%. By design

@rombarthelemy @DocEd FWIW, I think the quoted text above is an honest attempt to describe our best assessment of the truth. Seems like a perfectly reasonable interpretation of 95%CI. You might ask, why not do an another trial so you can find out? But I think opportunity cost of doing that is important. There are probably better things we could all do with our time and energy.

@kennethbaillie @DocEd sure but it is the case for every CI of every estimate of any RCT. So the most probable is that the effect is not clinically significant. And sure it’s time to move on. This sentence adds nothing but confusion

@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 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.