Actually, I think you are on to something here (even though the trial particpants are scoring themselves relative to their pre-illness self).
Technically they could get in with 4 questions where a symptom was worse/much worse than normal, but they would need to score "much worse than normal" on at least three of those four (i.e. 3x3, 1x2, 7x1). But I agree that people would not consider 18 a normal/healthy score. It's very similar to the SF-36 argument.
The CDC criteria asks for symptoms over the last 6 months. People can have temporary symptoms for all sorts of reasons e.g. Pre-Menstrual Syndrome/similar.
There is a lot of "exercise propaganda" in society - exercise is good for you. Then the CBT and GET participants get more of it as part of their treatment. I wonder could this influence them when answering questions about rating their health i.e. because they now have a regular enough walking routine (those that do, that is), would they feel that their health has improved even if they are still not working or not working much and by most people's standards aren't healthy.
So only 11% the SMC people who really didn't get much: http://www.pacetrial.org/docs/ssmc-doctor-manual.pdf were dissatisfied.
Confidence levels only make sense if you are dealing with normal distributions for which you have reasonably consistent parameters. This is not true. My example of sampling a population with a uniform distribution was intended to illustrate this. You could think of a uniform distribution as a piece of a normal distribution with an arbitrarily large SD. You could assign a wide range of values for population SD here. The distribution is not Gaussian (normal). What you are highlighting here is inconsistency. The underlying absurdity remains. None of those confidence values are meaningful.
These were numbers which caught my eye earlier. No matter what those running the trial decided about seriousness, the patients considered these negative events. It would be surprising if there were no correlation between adverse events and dropouts.
Confidence Intervals (CIs) is what they call them themselves underneath.
I never said otherwise. But one can't write in and say "your article is absurd" or "you fiddled the books"/"did do what you said you would in the protocol" or whatever without making specific points.
Now, since I'm not even sure what a 'profile of response' is, it's hard for me to interpret this. But it does look like might have set themselves up to 'specificy the profile of response' in advance and so allow use of the 95% confidence interval. Though to be honest, I haven't a clue what they're on about.
It might be possible to talk about confidence intervals for other distributions, but this is so unusual I would expect a bibliographic reference to mathematical research. The underlying theory of distributions requires familiarity with things like measure theory, Borel sets and Lebesgue integrals. Most mathematical distributions turn out to be nowhere differentiable. If Rieman integrals scare you, these things are serious hurdles. The overwhelming likelihood is that the assumption of Gaussian distributions is so deeply buried no one even imagined questioning it.
No argument there. I keep trying to find a way to push them into stating one of those unfounded assumptions on which this whole mess rests. If you artificially create a cluster through the sampling process, no one should be surprised that it spreads out afterward. At this point, the influence of those natural and artificial bounds takes over. Those subjects moving down are subject to different influences from those moving up, irrespective of treatment. The problem is how to get this across to possessors of invincible ignorance.
Thanks - missed that
Ok. Are they claiming they thought GET and CBT would come out better on everything? - doesn't seem like clinical equipose (if being awkward). They probably should have said when the threshold was being set at 0.01. Was it 0.01 when APT was against SMC (to make it hard for APT to seem better than SMC?) - would be dodgy if it was. I would presume they were saying at least some of them weren't 0.05. What's the point of mentioning the threshold of 0.01 if you're not going to use it. And if you stick to convention, you shouldn't use 0.05 for lots and lots of comparisons (I don't necessarily agree with the convention but they were the people who brought up 0.01).
In this case, they were saying fatigue was symptoms. While some of the secondary outcome measures were specific symptoms. So one might think that for symptoms, they might say they don't know and use 0.01.
