Alex Berenson has __published a gloating article__ citing a new __study - published in JAMA (the Journal of the American Medical Association____)__ - claiming to show that ivermectin has no significant effect on reducing severe disease progression in high risk patients with Covid (and note the gratuitous dig at Dr Robert Malone).

Here is the graphical abstract of the __JAMA article describing the study__ with the ludicrous use of donut pie charts to compare percentages to summarise their 'main result':

But it turns out that the JAMA article is yet another example of what can only be called scientific publishing fraud (similar stunts were used to discredit the use of hydroxychloroquine as explained __here__). The main paper obfuscates the results of the outcome we are most interested in: whether those given ivermectin ** were less likely to die** than those in the control group.

As pointed out on twitter by__ ____Jikky Kjj ____and ____Massimaux in a tweet reply to Pierre Kory__ (and in __this article by Jessica Rose__) only 3 the 241 patients given ivermectin (1.24%) died compared to 10 of the 249 patients in the control group (4.02%).

Using the standard (but useless and arbitrary) 'p-value' approach to statistical significance, this falls just under the level required to claim the result is 'significant'. This mortality information is crudely buried inside a paragraph on page 10 of 12 of the paper and is not mentioned in the abstract, results, or conclusions:

Note the ridiculous statement "*The ..mortality rate was similar for the ivermectin and control groups*". In fact, there's nothing 'similar' about them at all. A Bayesian analysis of the data** (assuming a uniform prior assumption for probability of death in each of the ivermectin and control groups) results in a risk ratio with median value 0.356 and 95% range 0.096 to 1.046. The probability that the risk ratio is less than 1 is just under 97% (it is 96.88%). In lay terms you can interpret this as a 97% probability that the true death rate for patients taking ivermectin is less than that of those who don't. And playing the game of ludicrous donut pie charts, here is what one for death (that they didn't include) looks like:

While the paper contains many tables with some bizarrely unnecessary information, the crucial mortality details (except for the obfuscated statement) **are not in the main paper at all**. You have to go online and find __Supplement 2__ and look for eTable 6*:

Unlike in the main paper, this table lays out the mortality data clearly, and also includes mortality by vaccination status. Why was this not included? Moreover this supplement, as well as the full article, do not appear to be 'open access' - I could only access them through a university subscription. Only the abstract, with its misleading results, is generally accessible.[**UPDATE**: *it appears you can now access the paper for free but you first have to create a free personal account*]. So people like Alex Berenson (and others who seek to discredit early treatments for Covid) are able to widely publicise misleading information to an unsuspecting public who will generally not see the real results for themselves.

JAMA need to be held to account for publishing the article in this form - deliberately hiding the most important results.

Here is a short video I made explaining the problems with the study (the problems are generic and as it's on youtube I had to avoid mentioning either Ivermectin or Covid otherwise it would instantly get banned):

Update: Steve Kirsch has written __an article about it__.

*It's also interesting to note that the paper ignores the fact that this properly randomized controlled trial really does show no significant decrease in mortality for fully vaccinated patients (7 deaths out of 254) than for the never vaccinated (5 out of 159).

** The Bayesian analysis - with full explicit assumptions and posterior distributions shown:

You have made a huge mistake. You say "It's also interesting to note that the paper ignores the fact that this properly randomized controlled trial really does show no significant decrease in mortality for fully vaccinated patients (7 deaths out of 254) than for the never vaccinated (5 out of 159). "

But of course this comparison cannot be made. The vaccinated and unvaccinated groups are not matched; subjects are not randomly assigned to the vaccinated or unvaccinated groups, and in fact those groups likely have very different characteristics. The randomization is only between the Ivermectine and Control groups.

I'm shocked that a professor of mathematics would make such a huge error.

Are you saying that the study is fraudulent, or do you just disagree with the conclusions? Because if you disagree with the conclusions, or have your own interpretation of the data, you implicitly agree that the study was conduced in an acceptable manner and that the results of the study are accurate.

How would the cause of death affect your interpretation of the results?

The little jab at the jab not making a difference in this study once hospitalised should be countered with the evidence that getting hospitalized in the first place is likely so much lower if you are fully vaccinated especially with a 3rd dose. https://abcnews.go.com/Health/covid-hospitalization-rates-omicron-wave-23-times-higher/story?id=82601041

The finding of more severe disease in the ivermectin group is not significant, but does show that ivermectin has very little if any effect on progression to severe disease (the data shows an inverse relationship but not significant). This part is looking at reasonably large numbers for both treatment and control. This is an important finding. Kory et. al. have made claims that if you take ivermectin you will not get sick, yet adherents have gotten sick and died. I am quite aware that the study was not looking at the early treatment side, but there is also the claim that in hospital treatment with ivermectin will stop progression to severe disease. It does not seem to be true.

And,â€¦

Indeed... define your outcome a priori as progression to severe disease, and then define progression to severe disease that gives the results you want once the data is in.