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I think we largely agree. The data does not suggest that there is no difference at all. It's not 100% random noise. I would summarise it as: the data suggests that there might be an effect. But the effect is quite small with poor separation between groups and no correlation with the Bell...

True, my graph looked weird because I checked precision below a certain value if the value was negative but above the value if it was positive. If I don't do this then I get the convergence to the ratio of ME/CFS patients in the total sample that you mentioned:

I tried to look at the precision level for different values for the VO2_percentage change. I had it switch around the 0 point (checked for smaller values in case of negative values and for bigger values when positive values were used). Here's the code I used df = df_max values = np.arange(-40...

Thanks @forestglip. Your coding skills are impressive and very useful. I think your analysis confirms that there might be a small effect. On the other hand, your approach is also iteratively looking for the best threshold, trying different things and looking for a value with the clearest...

Yes I think we mixed up the terms. You we're right about the sensitivity and specificity values, but your description (how many with lower values than -9.7% would be ME/CFS patients) refers to precision rather than specificity. Here's what I got for a threshold of -9.7, for example: Total...

Yes looks like an error with data entry - well spotted!

Thanks again for the impressive analysis @forestglip. I do not get the same results though. For example if I use the threshold of -9.7% for max VO2 values, I get 25 ME/CFS and 7 healthy controls so that ME/CFS patients make up 78% of the sample rather than 90%. Because of the large overlap...

Anyway, this is a bit besides the point. I plan to write a blog post about this because what the data shows is a quite different than what the paper reports and focuses on. - I think the data show that there is no significant effect for any of the outcomes, whether you look at AT or max...

I've now recalculated with the correct comparison of ME/CFS patients but it is still the same large difference: This calculation first takes the means, then expresses the change in means as a percentage (day2_MECFS.mean() - day1_MECFS.mean()) / day1_MECFS.mean() * 100 Result: 9.4% This one...

My bad, I was comparing the difference within ME/CFS to the difference between groups - will adjust my comment above.

I think that VO2 is VO2 divided per weight of the participant (so ml kg−1 min−1) while VO2_t is just the VO2 (ml/min), probably an error in the codebook. There's still something about these two values that don't add up because they should result in the exact same effect sizes but they often...

Thanks, got the same values as you. EDIT: this is an error see: https://www.s4me.info/threads/cardiopulmonary-and-metabolic-responses-during-a-2-day-cpet-in-me-cfs-translating-reduced-oxygen-consumption-keller-et-al-2024.39219/page-4#post-552976 I noticed that these average percentage changes...

Yes that was the difference. I forgot to include him because he had no valid data for HR. Yes I got the same result but made an error in writing it down in my table/overview. The different p-values might be due to me using: t_value, p_value = stats.ttest_ind(difference_MECFS, difference_HC...

Had a look but it seems that surprisingly only 10 participants did not meet the maximum effort criteria which are described as follows: Here's how I implemented this in my code (using Python) - hopefully somebody can check and try to replicate. df_original['HR_predicted'] = df_original['HR'] /...

Yes good point. I still have to look at these criteria so the values I reported above used all the data and will probably be quite different once I restrict the analysis to those who met the required thresholds.

I've tried to calculate the differences between CPET1 and CPET2 for each group and then compared them using a t-test. Here's what I got for the peak values: Most of the effect sizes are quite small and none will be statistically significant if one were to correct for multiple tests. Here...

I think it is splitting patients up in relatively active patients and passive patients. That's what the Dutch group of Bleijenberg et al. did. The focus for the active patients is then on stabilising their activity pattern and avoiding boom and boost while the focus for the passive group is on...

Seems that you are right: the legend under table 4 says: "%pred HRmax actual peak HR/(220-age)"

Thanks for the suggestion. Currently don't have plans to write about this in the near future but others have written about something similar (that Long Covid is not a functional disorder): https://www.statnews.com/2024/07/15/long-covid-not-functional-neurological-disorder/...

I think it might be a good idea but I also see potential problems. The vast majority of studies only report summary data (mean, sd, p-value) or in some cases just that the measurement wasn't significant. So the overview of data that is publicly available likely be a small subset of what has...