1/ Probably you have seen this. This got me scared for a bit, luckily I can say that my calculations say this is NOT the case. We have enough with the actual state of the data to add more sources of worry. Reason is complex, but I will try to explain. nejm.org/doi/full/10.10… Image
2/ The first thing I've noticed was that you are doing a rolling enrolling (which means that not everybody gets enrolled at the same time in the pregnancy) which is essentially what the last picture said. Suffice to say the number there is right, the interpretation is NOT.
3/ What do I mean? Well, between 50% to 75% of miscarriage's are reported to happen in the first 8 weeks. There is a caveat (as always) that many are not even clinically recognized. But we know that between 8% to 15% are. So that's better than nothing.
4/ Under normal situation with 3957 enrolled pregnancies you would expect between 316 to 593 total miscarriage's. But consider that between 237 to 445 happen on the first 8 weeks. When you then see 104 in 827 all the alarms set off!!!
5/ Why you may ask? Because the enrollment is not uniform. If you look at Table 3 only a few pregnancies receive the full risk during the study. Which means that those numbers are actually very high upper bounds. Image
6/ The key point that took me some time to figure out is that in this type of studies there is partial censoring of the sample. Explained to normal people, if there is gonna be an abortion the signal will come BEFORE than the actual signal of NO PROBLEM (baby is born).
7/ This means that the sample is naturally biased toward the adverse events appearing in partial studies. You would say, shouldn't the authors have to counter that in the publication? OF COURSE!!! Did I say that peer review is broken? No, this wouldn't have passed on my watch.
8/ Would the authors expected such a backlash? Probably not, because they have the complete sample, we have NOTHING, so I would question the journal why to publish a partial manuscript on June that has data up to end of February.
9/ All-in-all. There is still NO signal that the ratio of miscarriages would be higher than normal, at least not from this data; because when you account for the bias, you can estimate that you are actually halfway there. We will only know when the full study is published.

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More from @federicolois

2 Jul
1/ It was eventually retracted, as we pointed out with @LDjaparidze the logic was flawed BUT retraction should only be used for misconduct or requested by the authors themselves. mdpi.com/2076-393X/9/7/…
2/ Peer review is broken since earlier than SARS-Cov-2 and review board should be accountable because they allowed it to pass review for publication. In a sense this retraction is not authors fault, but the whole editorial board. ALL OF THEM.
3/ The reason why retraction is not the tools is that sometimes even bad papers provide good data. When a retraction is stamped into it, the data collected even if poorly interpreted gets destroyed. Bad paper, data pointing to new science. One example:
Read 5 tweets
20 Jun
1/n The power of the internet is incredible. Long story short. Back in 2018, I built a set of trading indicators, some of them truly novel stuff which I haven't published neither in source or a paper. tradingview.com/u/redknight666…
2/n Needless to say that I have been using them successfully since then, but something interesting happened yesterday. A user of Tradingview platform sent me this message. Some of my indicators happen to have some likes, and I always wondered how people used them.
3/n Obviously I said YES, bring it on. The indicator in question is a very strange indicator from a family I named as "Trend Denormalized". Most oscillators have an equivalent TA version. And in fact, I have and used successfully too the TA-RSI and other unpublished ones.
Read 5 tweets
13 Jun
1/n I have been sitting on this information for weeks on end, waiting someone to highlight it. But, if I tell you I will be subjected to 'being special' treatment. So, I wont give you the fish, I will teach you how to catch it.
2/n Resources and materials:
- [1] Total Coronavirus Deaths in Israel: worldometers.info/coronavirus/co…
- [2] National surveillance data: thelancet.com/journals/lance…
- [3] Supplementary: thelancet.com/cms/10.1016/S0…
3/n For better understanding you should be reading the whole [2] to understand what it means. I promise you it is worth the effort. I wont go anywhere (actually I will because I already wrote this).
Read 9 tweets
21 May
1/n I have my doubts that with all the emojis the real message suffered. The more you look for unknown coronaviruses inside pneumonia patients, the more we are going to find. "I believe there are many animal [coronaviruses] out there that can transmit to humans"
This is key.
2/n Why would you ask? Because there exist something called: "Pneumonia of unknown etiology"
And that number at least in this study is a whooping 54%.
3/n Someone told me the other day: "Most people are blissly unaware that the world is a harsh place. They don’t appreciate most of what is around us..." And that is what's going on. We are unaware that in more than half of the pneumonias we dont know what causes it.
Read 5 tweets
21 May
Mientras tanto nuestro presidentisimo @alferdez que tiene un grupejo de epidemiologos que parecen extras salidos de una revista condorito. Cierra todo por 1 semana, cuando haciendo esto mismo hoy es 1ero mundial en muertos diarios por millón. Ya lo dijo Einstein, insanity.
Que te quede claro, esto es una cuestión netamente política. Porque si miraran las estadísticas (y supieran lo que estan mirando) verían que ya se nota la desaceleración en las curvas de mortalidad y eso que tenemos 12 días de lag de datos.
Y algo más, a pesar de @FernanQuirosBA ser uno de los mas sensatos en toda esta locura, @horaciorlarreta comete el garrafal error de entregarle en bandeja la caída de la mortalidad que se vienen por mobilidad cerrando las escuelas. Sean inteligentes. @PatoBullrich @mauriciomacri
Read 4 tweets
11 May
1/n I was going to write a review myself, because this paper was hilarious, specially the twist at the end. @MIT_CSAIL discovers through observations that there is something called "Scientific Method". But the thread by @commieleejones is just gold. He deserves a follow.
2/n But I think there is never a waste of a good thread. So I encourage the authors @crystaljjlee, @arvindsatya1 et al. To research a little bit about the THE SCIENCE because well, its kinda lacking.
3/n It doesn't require much to someone that has been working in data for a living for 10+ years to figure out when something like this happens, there is something afoul going on.
Read 7 tweets

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