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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
Verified account
@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. Health Nerd‏Verified account @GidMK 28 Sep 2020
      Replying to @GidMK @saarwilf @Rootclaim

      The p-value point is a misunderstanding of the problem with small samples (it's not all about 'significance'). This is mirrored in the response to point 5 - this sample is FAR too small to run a multivariable logistic regression, all you'd get is noise if you adjusted

      2 replies 0 retweets 2 likes
    2. Saar Wilf‏ @saarwilf 28 Sep 2020
      Replying to @GidMK @Rootclaim

      It's the same: It is indeed hard to reach significance with logistic regression on small samples, but if the effect is strong, it can work. In this case it is, reporting 95% CI of 0.003−0.25. If this study's conclusion is wrong, it's definitely not due to the small sample size.

      1 reply 0 retweets 0 likes
    3. Health Nerd‏Verified account @GidMK 28 Sep 2020
      Replying to @saarwilf @Rootclaim

      It's not hard to reach statistical significance at all with small samples and logistic regression. What's hard is to see a meaningful result once you add in numerous 'control' variables

      1 reply 0 retweets 0 likes
    4. Saar Wilf‏ @saarwilf 28 Sep 2020
      Replying to @GidMK @Rootclaim

      Not sure I follow. Can you give an example of how their result could have been achieved by chance, at a probability >0.001?

      1 reply 0 retweets 0 likes
    5. Health Nerd‏Verified account @GidMK 28 Sep 2020
      Replying to @saarwilf @Rootclaim

      An example: there was underlying selection bias that led to a 'sicker' control group (however we define sicker). Given the small sample size, the logistic model cannot adequately control for this issue, and so the results are hard to interpret

      1 reply 0 retweets 1 like
    6. Saar Wilf‏ @saarwilf 28 Sep 2020
      Replying to @GidMK @Rootclaim

      Why doesn't it adequately control for it?

      1 reply 0 retweets 0 likes
    7. Health Nerd‏Verified account @GidMK 28 Sep 2020
      Replying to @saarwilf @Rootclaim

      It's pretty complex, but basically once you're into the single digits it's hard to draw much meaning from logistic models i.e.https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-016-0267-3#Sec12 …

      1 reply 0 retweets 0 likes
    8. Rootclaim‏ @Rootclaim 29 Sep 2020
      Replying to @GidMK @saarwilf

      They discuss overestimation of covariate-outcome relation. In our case where the covariates are the risk factors, it means the effect of VitD is actually larger. In any case, even if you assume only people with hypertension go to ICU, the result is significant (see our analysis).

      1 reply 0 retweets 0 likes
    9. Health Nerd‏Verified account @GidMK 29 Sep 2020
      Replying to @Rootclaim @saarwilf

      I mean, that's not true and it mostly misses the point so...I disagree. It really comes back to the fact that a tiny pilot study doesn't form a strong basis for decision-making

      1 reply 0 retweets 0 likes
    10. Saar Wilf‏ @saarwilf 29 Sep 2020
      Replying to @GidMK @Rootclaim

      You need to think risk/reward. What is the probability that the study is completely wrong, what is the risk of treatment, and weigh those to make a decision with positive expected impact on patient health.

      1 reply 0 retweets 1 like
      Health Nerd‏Verified account @GidMK 29 Sep 2020
      Replying to @saarwilf @Rootclaim

      I always find that attitude striking. HCQ was recommended on the same basis, and with arguably much better evidence, and now we know it doesn't work at all and we've wasted vast sums and potentially harmed millions. It is not such an easy either/or, I think

      4:23 AM - 29 Sep 2020
      1 reply 0 retweets 0 likes
        1. New conversation
        2. Saar Wilf‏ @saarwilf 29 Sep 2020
          Replying to @GidMK @Rootclaim

          HCQ was exactly how not to do science. No RCT, no history of effectiveness against respiratory viruses, no multiple correlation studies, no causal models, no clear mechanism of action. It's like rejecting vaccines, because doctors once used leeches and it failed.

          1 reply 0 retweets 0 likes
        3. Health Nerd‏Verified account @GidMK 29 Sep 2020
          Replying to @saarwilf @Rootclaim

          The HCQ proponents would argue precisely the opposite. And vitamin D has a long, long history of being touted as a cure for every disease under the sun and failing to show a benefit in rigorous RCTs

          2 replies 0 retweets 0 likes
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