When analyzing data from all the significant studies (that weren't retracted)

However, if you remove the Elgazzar paper from their model, and rerun it, the benefit goes from 62% to 52%, and largely loses its statistical significance. There’s no benefit seen whatsoever for people who have severe COVID-19, and the confidence intervals for people with mild and moderate disease become extremely wide.

Where does that leave us on the question of whether ivermectin works for COVID-19? Well, firstly, once you exclude Elgazzar from the research pool the current best evidence shows a fairly consistent lack of benefit. There are still one or two small, very positive trials, but in general ivermectin does not appear to reduce your risk of death from COVID-19.

Source: https://gidmk.medium.com/is-ivermectin-for-covid-19-based-on...

https://www.nature.com/articles/d41586-021-02081-w

And on the subject of IVMMeta

https://twitter.com/GidMK/status/1422044335076306947

"I've been talking about ivermectin a bit recently, and every time I mention it someone will link me to this odd website - ivmmeta dot com

So, a bit of a review. I think this falls pretty solidly into the category of pseudoscience"

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"The semi-anonymous site claims to be a "real-time meta analysis" of all published studies on ivermectin, collating an impressive 60 pieces of research

It's flashy, well-designed, and at face value appears very legitimate"

---

"The benefits that this website show for ivermectin are pretty amazing - 96%(!) lower mortality based on 10,797 patients worth of data is quite astonishing. Sounds like we should all be using ivermectin!

Except, well, these numbers are totally meaningless"

---

"Digging into the site, you're immediately hit with this error. That's not how p-values work at all, any stats textbook will show you why this statement is entirely untrue"

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"Most of these dotpoints are wrong in some way (heterogeneity causing an underestimate is particularly hilarious) but this statement about CoIs is wild considering that there are several potentially fraudulent studies in the IVM literature"

---

"Going back to the heterogeneity point, this is the explanation from the authors about why heterogeneity is not a problem in their analysis. They appear to have entirely misunderstood what heterogeneity is (hint: this is more about BIAS than heterogeneity)"

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"Also worth noting, I've previously shown the heterogeneity is high in meta-analysis of IVM for COVID-19 mortality, and that's almost entirely because there are 2 studies that show a massive benefit and a bunch of studies that show no benefit at all"

---

"Anyway, back to the website - the authors then present this forest plot of effect estimates

Each dot is a point estimate, and the lines around the dots represent confidence intervals"

---

"Now, any data thug will immediately notice something wildly improbable about this forest plot (H/T @jamesheathers
)

Can you see the issue? "

---

"While you have a think, here's a graph I made replicating these results. Not very pretty, but the final result is the same (with some minor rounding differences)"

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"Ok, so back to the question - why does this look problematic?

It comes down to confidence intervals. When you've got a bunch of very wide confidence intervals from different studies, you expect the point estimates to move around inside them quite a bit"

---

"Instead, look at those point-estimates! Even though they've all got MASSIVE intervals, virtually all the PEs are within 0.05-0.1 either side of 0.15"

---

"We can actually graph this. In Stata, I made what's called a funnel plot, which basically plots each point estimate against its standard error, with a line at the overall estimate from the meta-analysis model"

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"What you expect to see, if there are no issues, is an equal number of points on either side of the line at similar positions

Instead, ~virtually every point is below the estimate of the effect~"

---

"I ran an Egger's regression to test the statistical significance of this, and the result is that there is a huge amount of what would usually be called 'publication' bias in the results. In other words, this is extremely weird"

---

"What's happening here?

Well, this is where we really get into the weeds

You see, the meta-analysis on this website is REALLY BIZARRE"

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"How bizarre? Well, here are the measurements from the 'early' treatment studies - hospitalization is in the same model as % viral positivity, recovery time, symptoms, and death

All in the same model

WILD"

---

"Worse still, these appear to be picked almost entirely arbitrarily. The website claims to choose the "most serious" outcome, but then immediately says that in cases where no patients died or most people recovered a different estimate was used"

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"Even a fairly surface skim shows that what appears to actually be happening here is that the authors choose the outcome that shows the biggest benefit for ivermectin"

---

"For example, the analysis includes this paper. The primary outcome was viral load, which was identical between groups

Never fear however, because ivmmeta won't take "null findings" as an answer!"

--

"If you dig through the supplementaries, what you find is that for "all reported symptoms" there was a large but statistically insignificant difference, represented in this graph of marginal predicted probabilities from a logistic model. It is mostly driven by an/hyposmia"

---

"If you eyeball "any symptoms", you get the results that ivmmeta included in their analysis

But that's TOTALLY ARBITRARY. Why not choose cough (where there's no difference) or fever (where IVM did WORSE)"

---

"Also, hilariously, this study used the last observation carried forward method to account for missing data in symptom reporting. You can actually see this in the supplementaries - it's possible the entire result comes from a few people not filling out their diaries properly"

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"Also, hilariously, this study used the last observation carried forward method to account for missing data in symptom reporting. You can actually see this in the supplementaries - it's possible the entire result comes from a few people not filling out their diaries properly"

---

"Also, hilariously, this study used the last observation carried forward method to account for missing data in symptom reporting. You can actually see this in the supplementaries - it's possible the entire result comes from a few people not filling out their diaries properly"

---

"But the fun doesn't stop there. The inclusion criteria for this website is any study published on ivermectin, which has led to what I can only call total junk science being lumped in with decent studies"

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"Here's a study with impossible percentages in table 1 that used a comparator of 12 completely random patients as their control. They don't even say if these 12 people had COVID-19

Included in ivmmeta, no questions asked"

---

"ivmmeta includes all of the studies I've been tweeting about recently including this one

https://twitter.com/GidMK/status/1421368493975359490?s=20

And this one

https://twitter.com/GidMK/status/1420582871031373824?s=20

And this one"

---

"I've now read through about 3/4 of all the studies on the website, and I would say at least 1/2 of them are so low-quality that the figures they report are basically meaningless"

---

"Moreover, sometimes the website just does stuff that is wildly strange

Here's a study with no placebo control. They appear to have calculated a relative risk of...whether the patients in this hospital got treated with ivermectin? WHY"

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"I could keep going - there's just so much there. Even just the basic concept of combining literally any number from any study and saying that it makes the model MORE ROBUST is so intrinsically flawed

So. Many. Mistakes"

---

"But this thread is already too long, so to sum up

- the website looks flashy
- the methodology is totally broken
- I would call this pretty pseudoscientific; all the trappings of science, with none of the rigor"