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Disequilibrium, Rather than Postural Orthostatic Tachycardia Syndrome, Is the Primary Determinant of Orthostatic Intolerance in Patients with Long COVID Miwa, Kunihisa Background Orthostatic intolerance (OI) is an important factor affecting daily functional capacity in patients with long...

Autoimmune Disease is Associated with Heightened Long COVID Risk but Prior Immunization is Protective Objectives Patients with autoimmune disease (AD) may be predisposed to Long COVID, yet the impact of primary autoantibody-associated AD and prior immunization remains unclear. Methods...

Yes, I also am not sure if there was multiple test correction across the measures. They tested not only the seven measures between groups, but I think they also tested the association of each measure with each of six clinical metrics (mental health, physical health, sleep quality, disability...

Mm, I don't think the topic is intuitive enough for me to be able to respond in a way that actually helps, so I'll probably just leave it there. You may be describing a real issue, but I don't have the capacity to think about exactly what you're talking about right now.

I'm not entirely sure what you mean. Yes, various things can also be affecting the brain metric apart from the variables in the model, leading to additional noise and confounding, but this is an issue whether age is added as another covariate with ME/CFS status or not. If we add age, we can at...

I don't think it's circular. When we give age to the model, it can predict how much the brain metric would change due to age if ME/CFS status was held steady. For example, you can look at the 2nd plot above (the 1st 3d plot) and imagine the model is predicting a line for age but only in controls...

Ok, I did a bit of visualization to confirm and demonstrate why even if the groups are matched by age, controlling for age can still make the effect of ME/CFS status more significant. We can imagine that we have 20 controls and 20 cases, where each control has a matching case in terms of age...

It should apply to both. They may be weak associations with ME/CFS status, but still very strong associations with each other. For example, if the data for NII-RF was accidentally copied and replaced the data for NII-HR, making them identical, then NII-RF would still have a relatively weak...

If all the different niiXX outcomes are correlated to each other (which we might expect if these are possibly just different effects of a common pathology), then they will tend to have similar results. If the me/cfs_status association with NII-RF has that 0,1 significance pattern, then we can...

Hmm. I'd need to think about it more, but I think if they were well-matched for age, that would mean that without controlling for age, we can expect the predicted coefficient to be accurate (or at least not biased by age). But the variance due to differing age among the cohort (e.g. if some...

Yes, so I think if the strongest correlation is with age, controlling for it can remove so much noise that the increase in significance from better precision outweighs the decrease in significance due to decreased effect size you might expect from controlling for HADS.

I had to multiply by a much smaller number than 0.5 to decrease the correlation meaningfully. But here is the result if multiplying age by 0.05: The correlation is now 0.65, and still about a quarter of the time, it's only significant after adding covariates. But okay, 0.65 is still a high...

I'm not at a PC currently, so can't test it. But I think what's happening is that by increasing the influence of random noise, not only is the correlation of age with niiXX decreasing, but so is the correlation of mecfs_status with niiXX. In which case it's not surprising that mecfs_status is...

I haven't fully parsed this code yet, but I think there's an error here, calling model2 instead of model1:

Do we actually know if it increased differences? I don't see effect sizes or coefficients in the text. The predicted effect of ME/CFS status on the brain metric can decrease (which I agree, I think I would expect that with controlling for HADS), while the p-value still becomes even more...

What was in the paper? They were using real-life data where we're going into it not knowing if the outcome variable is actually dependent on mecfs_status or not. I showed that in the case where it is dependent on mecfs_status, the scenario we're discussing is possible.

We just did force it to happen. In the updated code I posted, in 92% of the trials where niiXX was not significantly associated with ME/CFS status (analogous to them showing no association for the univariate test in the paper), adding a covariate made it a significant association (significant...

This is because in the model you made, there is no relationship between ME/CFS and niXX, so it is all due to chance. niXX is just a function of age, so there should only be about 5% significant as false positives when testing the association with mecfs_status. If niXX actually depends on...

Several posts about Sjogren's were moved to: Is there a connection between ME/CFS and Sjogren's Syndrome? Discussion and a poll about testing

You replaced model1 <- lm(niirf ~ 1) with model1 <- lm(niirf ~ 0). 1 is the formula for an intercept. 0 means no intercept. From the R docs: So in the first updated code you posted, in the comparison of model1 and model2, it's comparing a model that just predicts 0 for every point with a...