I've recently gotten in touch with Alexandros Marinos, which has been writing lengthy critiques of Scott's most famous post, his review of Ivermectin.
After hearing his point of view, I must admit I've been swayed to the thinking that:
- There are some obvious statistical errors in Scott's post and his interpretation of Bitterman's epidemiological data
- The evidence for ivermectin, while poor, is about as reasonable as that for any repurposed covid drug (e.g. fluvoxamine), and even purpose-made ones (plaxovid)
While I fancy myself an off-label statistician I'm prone to error and maybe my interpretation is wrong. Given that Alexandros has recently finished this post which condenses his core criticisms, I was curious to see what people on LW thought about it.
Given that I have access to insider sources of information and a lot of inside data that I can't yet release publicly (you will have to take me on my word on this, sadly) it would be pretty bad form of me to make predictions other than the ones I have already made (many of which were made before I had that inside data):
The together trial suffered randomization failure: the placebo group is not concurrent, and that triggered a chain of events that led to it allocating disproportionately sick patients to ivermectin, and disproportionately healthy patients to fluvoxamine, with placebo being in the middle. This was amplified by several puzzling decisions by the together team. All this in a backdrop of indefensible dosing for ivermectin, and widespread community use in brazil, where it was available OTC.
I've summarized many of my concerns here: https://doyourownresearch.substack.com/p/10-questions-for-the-together-trial
And I've shared my model of what I think happened here: https://doyourownresearch.substack.com/p/together-trial-solving-the-3-day
There's a lot more to go over, but long story short, what I do doesn't involve a lot of probabilistic arguments, it's just logical inference for the most part, inference that anyone can replicate since I try to post receipts as much as possible. As a result, whenever I've had the chance to see internal data, it's matched my models pretty well.