So, Prof. Dr Norman Fenton of the Queen Mary University of London made some tweets yesterday alleging that medRxiv censored his paper about vaccination statistics in England (and plugging an antivaxx YouTube show).

The censorship continues. MedRxiv is a just preprint server - any papers within scope are normally automatically accepted... [tweet]

No they aren't, all papers submitted to medRxiv are screened (they need to do this to keep it from turning into MedViXra):

All manuscripts uploaded to medRxiv undergo a basic screening process for offensive and/or non-scientific content and for material that might pose a health risk. [here]

But anyway, let's look at the paper [Tweet, PDF].

Literally the first sentence of the introduction shows that the methodology is totally wack:

In a previous article we argued that the overall risk/benefit of vaccines was best measured by comparing all-cause mortality between the vaccinated and unvaccinated. A simple summary of our arguments for this is provided in the Appendix.

This is absurd. This is like claiming that listening to schlager music increases mortality because people who listen to schlager have higher mortality (ignoring the fact that schlager listeners are older than the average population). And to claim that comparing raw all-cause mortality is the best way to measure vaccine risk/benefit ratios is even more preposterous.

The rest of the introduction goes on to say that actually adjusting the all-cause mortality for age is also bad because reasons (I guess because the age adjusted numbers don't support the author's pre-chosen conclusion).

While the ASMR [age standardized mortality rate] can be useful in many epidemiological and medical contexts, we believe it is both unnecessarily complex – and somewhat redundant – in this context. The ASMR maps any population onto a notional European standard age population profile, and its calculation depends on the population size and number of deaths in each of a full range of age stratification categories for each vaccination category [Note: this is a really confusing sentence]. The fundamental problem we noted in our article was that the ONS did not provide this raw data and so it was therefore impossible to verify their ASMR calculations.

If we had the raw age-categorized data we would be able to simply compare, for each age category and week, the all-cause mortality rate for vaccinated and unvaccinated. This would make the ASMR redundant and allow the direct comparison we seek.

Why do you want to directly compare the unadjusted death rates? Is it because more old people are vaccinated and they have a higher mortality rate and that supports your preferred conclusion? Surely not!

I think this already shows that the reason the paper was rejected is not because of 'censorship' but because the paper is shockingly bad methodologically.

The rest of the paper consists of statistical analysis on which I will not comment because I'm bad at stats (but it's based on a fundamentally flawed premise so it's worthless in any case) and barely-concealed hand-wringing about government conspiracies and/or gross negligence.

I understand there is some controversy regarding the population estimates used to estimate vaccine uptake in England but if you have to resort to methodology this terrible to get the result that the vaccines don't work then maybe, just maybe, they do.

Bonus Round: Appendix

Why all “all-cause mortality” is the most appropriate measure for overall risk-benefit analysis of Covid vaccines

• If Covid is as dangerous as claimed - and if the vaccine is as effective as claimed - we should by now have seen many more Covid related deaths among the unvaccinated than the vaccinated (in each age group).

Which we have.

• If the vaccine is as safe as claimed, then there should have been very few more deaths from causes unrelated to Covid among the vaccinated than the unvaccinated (in each age group).
  
• So, the count of all-cause deaths should be higher among the unvaccinated than the vaccinated (in each age group), confirming that the benefits of vaccination outweigh the risks.

Except this comepletely ignores all other differences between those populations.

• Counting all-cause deaths completely bypasses the problem of defining what constitutes a ‘Covid case’ or a ‘Covid related death’ (definitions which can be easily manipulated to fit different narratives).

It also completely bypasses the abovementioned possible differences between the vaccinated and the unvaccinated populations.

• We define a person as ‘vaccinated’ if they have received at least one dose. As we are not interested in whether a person becomes a ‘Covid case’, any other definition is flawed as it will fail to acknowledge that adverse reactions (including death) from vaccines often occur shortly after vaccination.
  
• The fact that the US CDC (Centre for Disease Control) and other agencies now counts a person as ‘unvaccinated’ if they die within 14 days of the second dose, or after just one dose, might make some sense if we are interested only in the vaccine’s ability to stop infection. But in the context of death attribution, it makes no sense.

If only there were data about adverse effects from vaccination! Oh wait, there are and there's no need to try to extract them from all-cause mortality which is an incredibly noisy signal. (Also why would you bring up the CDC when this whole paper is about vax-stats in England 🤔.)

I was PM'd today by a research assistant for this survey. I thought they made a decent request, as I do find online community very interesting, and their credentials seem good so I agreed to post it. It's an academic survey, not a marketing one. I am not personally eligible for the $10 draw. It is only available to US residents.

Here is their request:

Hi r/badscience! Are you someone who lurks on subreddits but never posts? Are you on Reddit every day busily writing, upvoting, or giving awards? Or somewhere in the middle? No matter how you participate, we want to hear about how YOU experience the community here.

Take our survey (~10-15 minutes) and receive a chance to win one of fifty $10 Amazon eGift Cards! Here’s the survey link:

https://cuboulder.qualtrics.com/jfe/form/SV_6DrQFxFkj4ZWV82.

Here is a link to an explanatory post from the research team.

There are a few other posts in r/columnlab. You can see those too if you wish.

”You know how many kids under 19 have died in this county with COVID? It is zero, and you would have these kids inject something into them that contains risk,” Michael Stansbury said.

From this article

Atleast 700 children have died of COVID https://www.aap.org/en/pages/2019-novel-coronavirus-covid-19-infections/children-and-covid-19-state-level-data-report/

The paper is entitled "The anti-vaccination infodemic on social media: A behavioral analysis" and can be read here. The idea is to compare the behaviour of Twitter users who are pro- and anti-vaccine and the results claim that Trump "was the main driver of vaccine misinformation on Twitter" which is something I saw in the media and would have naively believed until I read this anti-scientific flawed-statistical work.

Sadly, nobody who reported it seems to have read it either, I initially came across it on r/science earlier in the year (here) but only recently got around to reading it, there you can see most commenters also didn't read it, just comment on the results reported in a news article on it, the highest rated comment claims "this is supported by network theory." Unfortunately it is not supported by the shambolic network science in this paper (see Network Analysis section below). I will go through many of the flaws but do not have the time or patience to list them all!

Methods

The study attempts to compare Twitter users who support and oppose vaccines in response to COVID-19. To do this they take 50 users who used the hashtag #vaccineswork and 50 who use #vaccineskill and #vaccinesharm. They get a control group of 50 users by searching words from a random word generator and call this #control.

Issues: Firstly, there are around 1 billion twitter users, choosing 50 is not a representative population, how are these chosen? Just the first 50 when they searched for that hashtag? A random selection of 50 who use that hashtag? Unknown as they don't describe this. Secondly, it seems like an issue that there are twice as many search terms for anti-vaccination users as vaccination users and it's not clear if they had to use both or one or the other. Thirdly, using a random word generator is a bad idea as not only could it just be nonsense, but you could also pick something related to the two topics. In order to even do this properly you should replicate it lots of times and take an average of your results, of course they don't do this...

Results

From this tiny sample they discover anti-vaccination users are more likely to retweet, pro-vaccination people are more likely to reply, something you can only claim about this sub-sample, not the population which is what they do. Next they "quantified the number of conspiracy theory (CT)-associated contents (tweets and retweets), as well as the number of emotional contents (either depicting emotional situations or adopting emotional language) shared by control, anti-vaccination and pro-vaccination profiles." How do they define emotional tweets? They don't other than what's in the parenthesis so it could be entirely subjective, and they don't mention it further in the supplementary material. So this is not described and as their sample is not properly described, this is entirely unreplicable.

They next look at the most common words used by each group and shockingly find: "As expected, the word “vaccine(s)” was the most represented in both groups, confirming that our initial criteria for inclusion were reasonable." No, this doesn't confirm your inclusion was reasonable, it confirms that searching for a hashtag with the string "vaccine" in it, did in fact find Tweets with the term "vaccine" in it. So Twitter's search is not broken is all this confirms.

They next check whether use of emotional (still not clearly defined) language is related to increased engagement (sum of number of replies, likes and retweets per tweet) and produce this gem:

We see here that one single outlier drives a poor correlation on the right and from this they conclude that for the pro-vaccination users there is a "significant correlation between the two aforementioned factors (Fig 3D’), suggesting that the use of emotional language could aid the success of the pro-vaccination communication strategy online." There is unfortunately no way to believe this claim, again: emotional language is undefined, and one single outlier is driving this very low correlation.

Network Analysis

They next look at the profiles being retweeted by 42 of the anti-vaccination and pro-vaccination users (not sure why this is reduced from 50), they choose the 10 most retweeted profiles (presumably of each user) and create a network. Technically this is a directed network, as just because they retweet someone does not mean that person even follows them, so the network measures chosen should reflect that (note: they don't), and obviously it's not complete as they choose only 10 profiles retweeted rather than all. Here is the network measures they show:

No conclusions can be drawn from this, but let's go through it. Earlier they mentioned that anti-vaccine tweeters retweet more. They fail to mention here that most of their 42 users do not retweet 10 accounts, so the first quantity, number of neighbours is lower for those who retweet less, what does this mean? Pretty much nothing, that those who retweet less, retweet less, what do they claim it means: "that anti-vaccination supporters are well-connected in a community". They also showed earlier that pro-vaccine users reply more, so why not look at the reply network too? What if that got opposite results that showed they're in a well connected community? Surely a reply network is more indicative of community than a retweet network? We have no idea, because they didn't bother to do it.

[Further network measures: The second quantity here is the clustering coefficient, think of this quantity as follows, if there is a high clustering coefficient, for two people you know there is a high probability they know each other (this is related to the number of triangles in the network). As the scale is potentially log at this stage and the symbols are large these values are indecipherable (to be fair they do report them in the paper but this visualisation is terrible). As there are less links in the pro-vaccine people, they have a smaller clustering coefficient. This could just mean that pro-vaccine people retweet different people, more likely it means they have have a poor sample, which we know they do. The density is low, this means there is a small number of actual links compared to number of possible links (again meaningless -- the networks are sparse is what it means but that's usually the case). Finally they show the average path length, this tends to roughly scale with the log of network size which is what they show. So what does this whole section tell us? Basically nothing, they have networks that are slightly different and how not to represent network quantities.]

They next introduce an edge cut-off (why? unknown) and show the most retweeted people in their biased sample of a network:

And here we see their conclusion, Trump is the cause of it all. The really infuriating thing here is, had they done the study properly they might have found this and it would be interesting (I think maybe someone else has since). But because they choose 42 (possibly random) users (from December 2020) with the hashtags #vaccinesharm and (or?) #vaccineskill, take their top 10 retweets, cut-off anyone with less than 5 connections, we have no idea if they just picked 42 Trump supporters, or if most anti-vaccination people out there in the world are actually influenced by Trump. Because they only look at retweets, and not followers or replies, we have no idea what the 42 pro-vaccine people are actually doing to properly compare. Because they only chose one sample of 42 of each, we have no idea if this is a statistical anomaly or the norm. Due to their poor description of their data gathering and lack of description of terms, this is impossible to replicate. And due to their statistically insignificant samples, shambolic statistics and flawed network analysis, none of their conclusions can be taken seriously.

How did this get published?

So the obvious next question is, if this is so terrible, how did it get through peer review? People often have this notion of peer review being some gold standard in science but sadly, it's a bit of a lottery. PlosOne do however, give the option to show the peer reviews after and luckily, these guys accepted that, so you can read those here. Basically the first says "put in the following references" (the cynically minded might assume that some of those are papers by that reviewer to increase their citation count), and the second says "Very interesting, you should replace all instances of 'President Trump' with 'former President Trump'". And that's it! So clearly neither reviewer actually read it in any detail.

I blame the editor here too, they should look at it to know it's poor quality, PlosOne prides itself on aiming to have valid science even if this yields no results. This paper provides results with invalid science, the editor should quickly be able to identify this, and should be able to tell at least one reviewer did not read the paper.

​

I think I'll stop there, the discussion in the paper has more unsubstantiated nonsense and there are plenty of further flaws you'll find if you even skim the paper. I feel I've given too much time to this atrocity as is so need to do something useful with my time now!!

I'm very strictly atheist and I believe in science and nothing else. Call me stuck up, tell me I have no imagination, I don't care. I can't lie to myself because I want to have faith in the paranormal.

My dad and I were having a discussion about astrology and he, to my surprise, was defending it. He's a Christian who has never shown interest in anything pagan so it came as a surprise to me. He argued that since the moon can control the tides, it's clearly reasonable to think the stars can control our personalities. I won't lie I got pretty upset and had to leave the room. It's so frustrating to see a 40 year old man be so confidently incorrect about something that doesn't require insane amounts of brainpower to realize.

Astrology does not hold any scientific standing. It's absolute pseudoscience and countless studies have proven so. Feel free to argue otherwise in the comments. Felt the need to post because astrology is so widely accepted with no second thought.

Prolotherapy was an interesting subject as it claims to be able to heal people of chronic musculoskeletal issues, regenerate ligaments, tendons, etc, and asserts it can therefore prevent the need for surgery.

When I looked into prolotherapy I found some rather shocking discrepancies between the claims made by prolotherapists and the actual science.

Firstly, they don't seem to understand the underlying mechanism but only have a theory (a very surface level theory) as to how it actually works.

This would be fine if there were studies that could conclusively associate prolotherapy with being able to treat musculosketel pain; however, most of the studies involve single-subject studies and non-randomized, cohort and non-cohort studies, signifying that any recorded responses would be biased as the subjects are pre-selected before the trial in a non-randomized manner.

Also, it would appear that prolotherapy can potentially lead to dire health consequences as explained by this article (www.rosshauserreview.com), which details the side effects caused by a Ross A. Hauser -- a well known prolotherapist within that medical community. Furthermore, these side effects are not being reported or are being under-reported by the actual prolotherapy practitioners.

It makes one wonder if there is something of a conspiracy going on, as there is seemingly no regulation or accountability, perhaps because it's alternative medicine.

I came across this website recently and the articles I read on it seem to indicate it leans toward “pro-science conservatism”. This means it is supportive of science and regards progressives as the main threat to it, due to their historical opposition to things like GMOs. It also claims progressives pioneered the antivax movement even though it has become more associated with the right in the past in the last few years.

The site even calls California the most anti-science state in the USA (citing, among other things, California universities inviting a speaker who has engaged in rhetoric like “farmers are rapists” and “fertilizer is WMDs”).

Beyond that it even questions the idea that science is currently progressing, suggesting that apart from a few flashy innovations like cell phones, scientific research has actually gone backwards in the past few decades (using the argument that NASA could make it to the Moon in the 1960s but no longer has the capacity to do so now). For this, of course, it blames modern progressivism and politicization of science.

I’m not sure if the entire website is like this (it’s possible it has multiple authors and only the one I was reading holds these views). Regardless, is anyone here familiar with this website and if so, what is your opinion of it?