10 ms·

Yes, this is strong. Worth noting the limitations though: > Randomization generated groups with comparable percentage of unfavorable risk factors as there was

by zamfi 6y ago

Yes, this is strong. Worth noting the limitations though: > Randomization generated groups with comparable percentage of unfavorable risk factors as there was no significant difference in subjects with at least one risk factor, except for high blood pressure and diabetes mellitus, known risk factors for unfavorable disease progression [2], which were more frequent in patients not treated with calcifediol. These are HUGE risk factors. Also: > This pilot study has several limitations as it is not double-blind placebo controlled. On the other hand, in the first studies evaluating risk factors for severe disease and/or death from COVID-19, the possible role of obesity was not considered. Therefore, given the isolation characteristics of the patients, we did not collect the BMI, which would have allowed us to add obesity as a risk factor for severe evolution of COVID-19 [37] It is striking to consider that obesity shares with aging and black or asian ethnicity a surprising overlap as risk factors for severe COVID-19 and vitamin D deficiency. Yeah, BMI would've been nice too. Still, check out table 2. Even with these limitations, seems powerful.

acqq 6y ago
The group receiving Calcifediol had more "no bad risk" patients: "At least one prognostic bad risk factor(@) Group receiving Calcifediol: 48% Group without Calcifediol: 61.54%" "@) Patients with at least one of the following risk factors (age >60, previous lung disease, chronic kidney disease, diabetes mellitus, hypertension, cardiovascular disease or Immunosuppressed and transplanted patients)." That's what can be concluded from "at least one": (52% had no bad risk in D group, but only 38.46% had no bad risk in the other group). But it is also not clear where there were more patients with "multiple" factors! Or if they were those who had more problems at the end. The whole paper contains neither raw data nor any graphs and only means and standard deviations, as far as I see? I would personally really like to see the graphs of distributions or to use raw data to check myself. The critical question is how good the randomization was done.
- deleted 6y ago
  [deleted]
- mlyle 6y ago
  Randomization doesn't ensure perfectly balanced groups-- it just ensures that the imbalances of variables you don't measure (including things that change after randomization not related to your intervention, with blinding) are drawn from a distribution that you can apply rigorous statistical reasoning about. Yes, we can see some things are a little unbalanced. But the effect is so massive: we might see 4/5 of the control's rate of ICU admissions if the "one prognostic bad risk factor" determined outcome entirely; instead, we see 1/50 of the rate.
  - acqq 6y ago
    > we might see 4/5 of the control's rate of ICU admissions if the "one prognostic bad risk factor" determined outcome entirely But we still don't know if the persons with multiple bad risk factors were those who ended with bad outcomes? As far as I understand, if it was like that or not can't be seen from the paper at all, and I can imagine that it could have happened. I would really prefer the more raw data to the tables with the selected means and deviations given.
    - mlyle 6y ago
      You have to get very, very unlucky on the dice rolls to get samples that are so tilted-- to pick 26 people out of 76, and somehow come up with 13/14 of those requiring ICU in the smaller group, if vitamin D has no effect. Outside of deliberate rigging... (it might even be difficult to deliberately sort and get this much of an imbalance). Indeed, checking that the two groups look similar after randomization is completely optional. This study does an OK job of doing so. There's both known and unknown reasons why someone might be predisposed to have a bad outcome. The reason we randomize, rather than try and make "balanced" groups, is that it addresses both unknown and known factors. Yes, you can, by chance, get more people who are going to have a bad outcome in one group; about 5% of the time you'll get a p<0.05 finding this way. :P
altvali 6y ago
Is BMI a good indicator of obesity, though?
- purec 6y ago
  It works for the average person but becomes distorted when you look at athletes, especially those that need to put on more muscle.
- SketchySeaBeast 6y ago
  I understand the objections to BMI comparisons at an individual level (though I believe people think themselves a bit too exceptional too often), but as part of a larger study, I would think it'd make a good additional data point, no?
  - altvali 6y ago
    It most definitely would, but measuring body fat percentage would be better.
    - SketchySeaBeast 6y ago
      But then you couldn't easily extrapolate to the population. Well, you could, but you'd require the population to have an understanding of their body fat percentages. BMI isn't ideal, but it's quick and dirty, easy to collect and it should be relatively reliable for the population.
      - greenbush 6y ago
        It's not that straightforward as the BMI thresholds vary by ethnicity: https://www.hsph.harvard.edu/obesity-prevention-source/ethnic-differences-in-bmi-and-disease-risk/ https://www.hsph.harvard.edu/obesity-prevention-source/ethni...
- graeme 6y ago
  It’s actually pretty decent. It can make errors in the case of people with extreme muscle mass, or very low muscle mass for their size, or the very short, or the very tall. But for most people, if you’re over 25 bmi you probably could lose some weight. And at a population levels the errors above average out, even in a smaller group. People make a big deal over the exceptions to it while ignoring that it is broadly accurate and that exceptions are not as common.
Alex3917 6y ago
> These are HUGE risk factors. Given that Vitamin D deficiency can cause high blood pressure, their decision not to correct for this might be the better option.
- zamfi 6y ago
  Good point. Confounds all the way down!
  - deleted 6y ago
    [deleted]

by zamfi 6y ago

acqq 6y ago

The group receiving Calcifediol had more "no bad risk" patients: "At least one prognostic bad risk factor(@) Group receiving Calcifediol: 48% Group without Calcifediol: 61.54%" "@) Patients with at least one of the following risk factors (age >60, previous lung disease, chronic kidney disease, diabetes mellitus, hypertension, cardiovascular disease or Immunosuppressed and transplanted patients)." That's what can be concluded from "at least one": (52% had no bad risk in D group, but only 38.46% had no bad risk in the other group). But it is also not clear where there were more patients with "multiple" factors! Or if they were those who had more problems at the end. The whole paper contains neither raw data nor any graphs and only means and standard deviations, as far as I see? I would personally really like to see the graphs of distributions or to use raw data to check myself. The critical question is how good the randomization was done.

deleted 6y ago
[deleted]
mlyle 6y ago
Randomization doesn't ensure perfectly balanced groups-- it just ensures that the imbalances of variables you don't measure (including things that change after randomization not related to your intervention, with blinding) are drawn from a distribution that you can apply rigorous statistical reasoning about. Yes, we can see some things are a little unbalanced. But the effect is so massive: we might see 4/5 of the control's rate of ICU admissions if the "one prognostic bad risk factor" determined outcome entirely; instead, we see 1/50 of the rate.
- acqq 6y ago
  > we might see 4/5 of the control's rate of ICU admissions if the "one prognostic bad risk factor" determined outcome entirely But we still don't know if the persons with multiple bad risk factors were those who ended with bad outcomes? As far as I understand, if it was like that or not can't be seen from the paper at all, and I can imagine that it could have happened. I would really prefer the more raw data to the tables with the selected means and deviations given.
  - mlyle 6y ago
    You have to get very, very unlucky on the dice rolls to get samples that are so tilted-- to pick 26 people out of 76, and somehow come up with 13/14 of those requiring ICU in the smaller group, if vitamin D has no effect. Outside of deliberate rigging... (it might even be difficult to deliberately sort and get this much of an imbalance). Indeed, checking that the two groups look similar after randomization is completely optional. This study does an OK job of doing so. There's both known and unknown reasons why someone might be predisposed to have a bad outcome. The reason we randomize, rather than try and make "balanced" groups, is that it addresses both unknown and known factors. Yes, you can, by chance, get more people who are going to have a bad outcome in one group; about 5% of the time you'll get a p<0.05 finding this way. :P

altvali 6y ago

Is BMI a good indicator of obesity, though?

purec 6y ago
It works for the average person but becomes distorted when you look at athletes, especially those that need to put on more muscle.
SketchySeaBeast 6y ago
I understand the objections to BMI comparisons at an individual level (though I believe people think themselves a bit too exceptional too often), but as part of a larger study, I would think it'd make a good additional data point, no?
- altvali 6y ago
  It most definitely would, but measuring body fat percentage would be better.
  - SketchySeaBeast 6y ago
    But then you couldn't easily extrapolate to the population. Well, you could, but you'd require the population to have an understanding of their body fat percentages. BMI isn't ideal, but it's quick and dirty, easy to collect and it should be relatively reliable for the population.
    - greenbush 6y ago
      It's not that straightforward as the BMI thresholds vary by ethnicity: https://www.hsph.harvard.edu/obesity-prevention-source/ethnic-differences-in-bmi-and-disease-risk/ https://www.hsph.harvard.edu/obesity-prevention-source/ethni...
graeme 6y ago
It’s actually pretty decent. It can make errors in the case of people with extreme muscle mass, or very low muscle mass for their size, or the very short, or the very tall. But for most people, if you’re over 25 bmi you probably could lose some weight. And at a population levels the errors above average out, even in a smaller group. People make a big deal over the exceptions to it while ignoring that it is broadly accurate and that exceptions are not as common.

Alex3917 6y ago

> These are HUGE risk factors. Given that Vitamin D deficiency can cause high blood pressure, their decision not to correct for this might be the better option.

zamfi 6y ago
Good point. Confounds all the way down!
- deleted 6y ago
  [deleted]