By many measures, John Ioannidis is a lion of medical science. The Stanford University professor is the author of some of the most cited journal articles in medical history. His research in statistics and biomedicine has arguably changed the practice of medicine. A 2010 article in The Atlantic said “Ioannidis may be one of the most influential scientists alive.” And you would never know any of this from reading comments about him today.
It started on March 17, when Ioannidis published an opinion essay in STAT saying that the data on Covid-19 were not sufficient to know the disease’s true prevalence and fatality rate. He also argued that scientists were in the dark with respect to which distancing and lockdown measures work, which don’t, and what the measures’ downstream harms might be. All of which was true — even if his very early estimate that as few as 10,000 might die in the U.S. turned out to be wrong. Ioannidis went on to say that preventing people from working or leaving their homes could cause more harm than the virus itself.
It was this last bit that set off the firestorm of criticism. A typical comment on Twitter came from Richard Ebright, a professor of chemistry and chemical biology at Rutgers University, who called the essay “content-free, logic-free drivel.” For detractors, Ioannidis’ argument that the data on both the virus and lockdowns were insufficient was tantamount to saying, “Do nothing.”
As swift and punishing as the reaction was, it was perhaps unsurprising. Science can be a rough and tumble arena, where researchers sling arguments at one another with abandon. They routinely criticize one another’s methods, statistical analyses, and conclusions. And in this case, the stakes were extraordinarily high. One early estimate suggested that 2.2 million people in the U.S. alone would die if Covid-19 was left unchecked.
But the attacks on Ioannidis from the medical community and the popular press are different. They are tinged with partisanship, with each side appearing to embrace the math that serves their political allegiances. Ordinarily sober-minded researchers have attacked Ioannidis’ methods with hyperbolic and emotional arguments that suggest it’s not so much his science but his questions that they dislike.
In politicizing Ioannidis’ work, both the left and the right have failed to understand or acknowledge the critical questions he is raising. In doing so, they impede our ability as a nation to respond in a way that minimizes deaths, not just from the disease, but from our reaction to it.
From the beginning of the pandemic, there has been uncertainty surrounding two crucial pieces of information about Covid-19: its prevalence, or the percentage of the population infected with the disease; and its infection fatality rate, the percentage of infected people who die. Many early estimates placed that rate at around 1 percent, roughly 10 times deadlier than the seasonal flu.
In April, a couple of weeks after Ioannidis’ essay appeared in STAT, he and 16 other researchers conducted a study in Santa Clara County, California, to better define the infection fatality rate. The researchers tested the blood of 3,330 participants and found that 50 had antibodies to Covid-19, indicating that they had been infected with the virus — many of them without even knowing it. This finding suggested that the prevalence of Covid-19 in Santa Clara County was far higher than official counts indicated, and that the infection fatality rate was therefore much lower than previously feared, only around 0.17 percent. (The original version of the manuscript gave the fatality rate as ranging between 0.12 and 0.2 percent.) It followed that extended lockdowns might not be the best approach to controlling harm from the virus.
Although the Stanford group’s estimation of the infection fatality rate falls at the low end of the range obtained in other antibody studies, it is not an outlier. A meta-analysis by Ioannidis found that other groups have independently estimated similar fatality rates. Even the Centers for Disease Control and Prevention has placed the case fatality rate at 0.4 percent, meaning the infection fatality rate is substantially lower. Yet the Santa Clara study was singled out for particularly pointed attacks.
Some critics suggested the Stanford researchers had biased the study toward finding a high prevalence, to justify Ioannidis’ argument that the initial estimates of fatality rates were inflated. Other critics said the antibody test used in the Santa Clara study was so unreliable that it was possible none of the 50 participants who tested positive had actually been infected. This, despite the fact that almost all surveys to date suffer similar test-reliability problems in low-prevalence areas.
Still others argued that the Stanford team’s decision to recruit subjects through Facebook possibly skewed the sample group toward healthier and wealthier individuals, though there was hardly a peep of criticism for a New York State survey that recruited test subjects at grocery stores and shops or a Los Angeles County study that skewed toward the rich.
The Santa Clara study caught on in the popular press, where opinion-makers and reporters quickly took sides. The right-leaning press was quick to embrace the idea that the threat of the pandemic had been exaggerated. Ioannidis earned praise in the National Review. The DailyWire.com, a right-wing news and opinion website, pronounced that liberals had been “continuing to gaslight” the public with inflated pandemic numbers and called for an end to “dystopian” lockdowns. Fox News’ Laura Ingraham, a conservative firebrand, had Ioannidis on her show.
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Thoughts or questions on Covid-19? |
On the left, The Nation published an article calling Ioannidis’ work a “black mark” on Stanford University. The source of the Nation’s ire was an article in left-leaning media outlet BuzzFeed News, by Stephanie Lee, suggesting that Ioannidis had failed to disclose a potential conflict of interest in the Stanford study. Wrote Lee, “Ioannidis and his coauthors did not disclose that the study was funded in part by [David] Neeleman,” whose company JetBlue potentially stood to benefit from research indicating that the threat of Covid-19 had been overestimated. Quoting from the anonymous whistleblower complaint, Lee wrote, “Concern that the authors were affected by a severe conflict of interest is unavoidable.” With that accusation, even some longtime medical allies of Ioannidis began to scatter.
But it turned out that the backlash was missing crucial context. Neeleman did indeed make a $5,000 donation to the study, but it was placed by Stanford in an anonymous fund that the university says was created to ensure the independence of researchers. Ioannidis told us that he only learned of the $5,000 donation when the article in BuzzFeed appeared. He said, “I and many others invested our time and effort to the project entirely for free.” Although Eran Bendavid, the study’s lead investigator, says he did correspond with Neeleman while the work was ongoing, he told us he only learned of Neeleman’s donation from an email from the Stanford development office. That email, which he shared with us, was dated April 15, one day after the study had been completed and submitted for publication on the MedRxiv preprint server.
Attacks on Ioannidis came early and often. Just days after the study published, Columbia University statistician Andrew Gelman wrote that Ioannidis and his co-authors “owe an apology not just to us, but to Stanford.” And in May, YouTube pulled one of Ioannidis’ interviews, saying that it contained “medical misinformation.” Despite numerous queries, neither YouTube nor its parent company, Google, has revealed which part of the interview could be construed as misinformation.
The politicization of medicine and science is nothing new. But some people seem to think this tribalism is owned by the political right — that the rightwing leverages science as propaganda, while the left engages in “discussion and debate.” But in the case of Ioannidis, the nuance has been lost on both sides.
Ioannidis’ views on lockdowns, far from aligning with a Trumpian desire to benefit Wall Street, are consistent with his longstanding body of work, which has regularly pointed out how researchers often downplay or ignore the harms of medical interventions. As he wrote to a private list of doctors and researchers, “Locking ourselves in our beautiful mansions and continuing with our videoconferences practically does nothing for nursing homes and chronically badly prepared hospitals . . . It also kills the poor, the disadvantaged . . .” He has cautioned that protracted lockdown will cause starvation, violence, poverty, and deaths that could exceed the number of lives saved by avoiding Covid-19 infections.
He’s not alone in these concerns. The World Food Program estimates that 265 million people worldwide could face hunger and starvation due to lockdown-related disruptions in the food supply. Business writer Tom Keane suggests, based on a study linking death rates to unemployment, that pandemic-related job losses in the U.S. alone could translate to an extra 815,000 deaths over the next 10 to 17 years.
Ioannidis will almost certainly emerge from this imbroglio with his reputation as a rigorous researcher restored, but the level of vitriol being aimed at his work is causing others in the medical community to self-censor, as a number of doctors and researchers have acknowledged to us. This is the worst possible outcome during a pandemic when there is so much yet to be learned.
Ioannidis, right or wrong, has raised difficult questions, in the best tradition of science. Silencing him is an enormous risk to take.
DISCLOSURE: Both authors of this op-ed, Jeanne Lenzer and Shannon Brownlee, have previously co-authored published work with the subject of the piece, John Ioannidis.
UPDATES: Through a series of good-faith oversights and miscommunications, a full accounting of the prior publishing relationships between each co-author and Ioannidis was not included with this piece at the time of publication. Jeanne Lenzer, the lead author, had disclosed co-authorship with Ioannidis, but Undark initially failed to include this disclosure with the published article. That has since been corrected. These omissions were inadvertent. In addition, an earlier version of this article incorrectly implied that a critique of John Ioannidis’ work by Columbia University statistician Andrew Gelman was published after other critical articles had appeared in the popular press, including two articles published in mid-May by Buzzfeed and The Nation. Gelman’s critique, published on a Columbia University blog and devoted to statistical modeling and social science, appeared on April 19. The story has been updated.
Jeanne Lenzer is a medical investigative journalist and the author of “The Danger Within Us: America’s Untested, Unregulated Medical Device Industry and One Man’s Battle to Survive It.” Shannon Brownlee, MSc, is senior vice president of the Lown Institute, a nonpartisan health care think tank based in Boston, and author of “Overtreated: Why Too Much Medicine is Making Us Sicker and Poorer.” She is a former member of the Undark advisory board.
Comments are automatically closed one year after article publication. Archived comments are below.
I wonder how much more has to happen before John and his friends actually show shame at how much they got wrong?
The other co-author (who is apparantly on your advisory board, seems to have failed to advise you that they also co-wrote an article with Ionnaidis. See here: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.12834
Took me 2 minutes and you could have just asked them especially after the other author was found to have failed to reveal their link to him.
This article really has failed at the first hurdle of basic fact checking.
Thanks for bringing this to our attention, Mark. We have further clarified the disclosure — and the original correction — to note that both authors have previously collaborated with Ioannidis. This should have been underscored at the time of publication, and we regret the omission.
It was actually picked up on Twitter by another user who seems to have noted it before I did actually.
“all of which was true — even if his very early estimate that as few as 10,000 might die in the U.S. turned out to be wrong.”
He wasn’t wrong. Just because more died doesn’t mean “as few as” X number “might have” died under different circumstances.
Considering the death toll will pass 500,000 soon, and considering he was explicitly arguing against lockdowns and other strong measures, under what circumstances can you possibly imagine the death toll would have remained below 10,000?
Major factual error in the article: The CDC estimates the infection fatality rate from known and unknown cases is 0.5%, not the case fatality rate of only known cases as stated in the article – same source Table 1
I just can’t stand listening to each side avoid valid, pertinent, important questions all the while judging anyone who disagrees with them. I can’t watch it anymore. I wonder if we will ever really get the whole truth. It amazes me that they don’t seem to want to figure it out. They seem to only want to shove the “Gate’s Plan” down our throats and we are supposed to accept it without question.
Politicization as substitute for fact-checking has infected an increasing array of topics of public importance.
Does HIV cause AIDS?
Does carbon dioxide cause global warming?
On both of these matters, right-leaning politics is skeptical or even answers “No”. Left-leaning politics asserts that the answer in both cases is “Yes” and that “the science is settled”.
However, the facts in the primary research literature do not unequivocally support a plain “Yes”. Those facts cannot be compressed into the space of a comment but I would be happy to cite the sources in response to e-mail enquiries.
I disagree. Yes, they used a quite high IFR in the model – but not an irrationally high one. It might be too high under “normal circumstances”, but when you look at the the really hit regions like some cities in IT, ES, FR, US/NYC, the real IFR there is in this range. They were not that far off. The point is: if nothing is done to control the wave health system *is* stressed anf IFR goes up badly. So yes, under perfect circumstances (not many patients, etc) IFR may be around 0.4 or even lower. But that is just the case as long hospitals are not overwhelmed, staff can still take care etc.
Yes, they calculated the worst case scenario – but not a unrealistic one. And at that time they had data from IT, CN, KR
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/
> If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths.<
I understand the estimate of 0.3 % at that time because not everyone from the ship who died later already had died at that point (but many had been at this time in critical condition), but I do not get why even assume that just 1 % would get infected. Yes, there are theories about cross-immunity, but can one assume it?
The criticisms of this article in the other comments are valid. Portraying the scientific and statistical critiques as largely partisan is false. The authors had a weak result and went to the media. This would be wrong if they went to the left or right media.
And sure, John seems to be a nice person. I can’t say that Laura Ingraham or Tucker Carlson seem all that nice, though I haven’t met either one.
This was a difficult article to read. All these accusations of unscientific partisanship but no real understanding of the scientific issues at the heart of the controversy.
Full disclosure: I’m the guy who sent Gelman the email that kicked off his initial post on the Santa Clara study. That’s not to say these issues weren’t spotted by others, only that I had a bit-player role in this story so I’ve paid attention to it.
I mostly want to make a point about these accusations of partisanship. The morning I wrote to Gelman was a Saturday or Sunday, and I was settling into my office to get caught up on work I’d fallen behind on the previous week due to stressful covid-related home-school duties with my school-age children. I did a quick scan of google news and saw a tantalizing headline related to the Santa Clara study. Desperate for the Covid nightmare — especially the school closures — to be over, I clicked through and read the preprint. I was genuinely rooting for the results to hold up and was genuinely saddened when I spotted the glaring problems. Realizing I should get back to work, I quickly typed up my thoughts and emailed Gelman. You know the rest.
My main point is that there was nothing partisan in my intentions. I was genuinely rooting for the authors to be correct and for me to be missing something. Likewise, Gelman’s post and subsequent discussion were studiously even-handed if you actually read them.
Brian, John:
I agree Gelman gave an excellent substantive critique of the original paper. I didn’t read this piece as addressing Gelman so much as a large chorus of less substantive responses speculating about Ioannidis’ motives, or being outraged at the very idea that there are tradeoffs, or being angry because right-wing sites promoted Ionnidis’ study.
So I think this article correctly and usefully points out tribalism where we have a harder time seeing it. Tribalism that gets in the way of substantive discussion like Gelman’s.
Ioannidis shares the blame. Even aside from his uncharacteristic method goof, he invited the storm by going quickly from his guesstimate to a strong presumption of overreaction, skipping past objections based on other estimates (e.g. NYC’s rate) and objections based on precaution (plausible R0 estimates still included unacceptably high values, urging action to buy time).
Maybe even so it would have been fine if he weren’t John Ioannidis. But he cannot escape his position. Imagine a CEO idly wondering on camera if “perhaps we’ve hired too many people”.
Great comment. Agree all around.
Your article is criticized here: https://statmodeling.stat.columbia.edu/2020/06/13/fake-mit-journalists-misrepresent-real-buzzfeed-journalist-maybe-we-shouldnt-be-so-surprised/
It seems that you misrepresented undark as “MIT’s Science Magazine” in your email.
Does either of you have any affiliation with MIT?
If Undark is really “MIT’s Science Magazine”, why doesn’t it mention MIT anywhere on its website, and why doesn’t MIT mention it on its website?
What is the basis for representing yourself as such?
You should diacuss issues raised in whole of the Buzzfeed article – specifically, that two researchers involved in the Santa Clara study withdrew because of ethical issues related to validity of the testing. The researchers recruited under a false promise an “immunity passport” if participants tested positive for antibodies, and didn’t accept the ethical responsibility to retest participants if they tested positive, despite that there would likely have been a high percentage of false positives. IOW, they ignored ethical implications of participants who were infected falsely thinking they had a green light to visit grandma.
Additionally, the recruitment process, with a wife of a researcher emailing inducements to go get tested, clearly violated reasonable ethical standards for human subject research.
These are all relevant issues to the criticisms of the SC study. It is extremely disappointing that you left out any related discussion. To do so suggests a bias on your part to defend Ioannidis and co-authors. To gloss over such issues and then suggest that Ioannidis is an innocent pure scientist being attacked by partisans is irresponsible journalism.
That isn’t a defense of the attacks, but Ioannidis has taken on the role of public policy advocate who was relying on flawed science and highly dubious methodology. As such, personal attacks while maybe not defensible, shouldn’t be seen as out of left field (pun intended).
This article is a sham and is full of misinformation. A lot of that misinformation is summarized here (https://statmodeling.stat.columbia.edu/2020/06/13/fake-mit-journalists-misrepresent-real-buzzfeed-journalist-maybe-we-shouldnt-be-so-surprised/). I noticed many of these things on first read before Gelman’s post. Aside from all of this BS listed in the link-Ioannidis keeps embarrassing himself by putting out cherry-picked studies of, eg, IFRs of healthy young blood donors as examples to be compared to population-wide numbers. It is sad since England is approaching (and will hit soon) 0.1% deaths for the ENTIRE country, and we can be damn sure the seroprevalence there is not above 10% country-wide. Everyone is already ignoring Ioannidis and knows what the numbers are. Much of the calling out is in no way “political,” it is just the pointing out of unfortunate and inconvenient facts. On the other hand this “opinion piece” (by the surely unbiased coauthor of past work with Ioannidis) is one of the worst “pot to kettle” examples in a long time.
Dave – You give away your lack of scientific fortitude by referencing that the COVID-19 fatality level for UK is approaching 0.1% of the entire population and that this is implicit evidence of an IFR orders of magnitude greater (i.e. seroprevalence not even 10%). However, despite you pointing out a biased approach used elsewhere (e.g. study using healthy blood donors), you completely ignore the bungled effort of the NHS in dealing with the most vulnerable (i.e. those over 80 and those with severe comorbidities). The exact same phenomenon (along with similar numbers/rates) can be found in NY, due to the exact same response. You cannot, without exposing yourself as a charleton, use bias as an argument against, while using bias to support said argument.
How so? 42K people have already died in the UK as a result of COVID-19 according to JHU. Projected deaths as of end of September (https://covid19-projections.com/united-kingdom which has been very accurate) about 52K. Population of UK-about 66M. So currently 0.064% of total population has died, likely to rise to about 0.08% by end of summer. Not quite 0.1% (I said approaching) but close. This is presumably with, what, at most 15% affected? That is likely a serious over estimation given what we have seen from testing in various countries around the globe, and the total fatality rate is probably underestimated.
What in the world is inaccurate about that? Further, no one is saying that efforts to protect the most vulnerable have not been bungled. However just like Ioannidis, simply stating that “if we just protected nursing homes” everything would be fine (or whatever the argument) is like saying “if we removed all of those who are likely to die then the CFR will be low.” It is a vacuous argument. Maybe some good would have come from Ioannidis’ blather if he simply gave realistic plans for how that could be done.
Excellent article and most inspiring for any scientist seeking the trurh. As for Ioannidis, with IFR as low as 0.26 according to the CDC there is no doubt he is already vindicated. Anyone else would have been celebrated already as a new Prometheus.
Pointing out the lowest possible IFR is still 250% higher than what Ioannidis estimated isn’t exactly vindication.
And unless half of Americans are already infected, even that number appears almost certain wrong. Somewhere in the range of 0.4-09 still appears to be most accurate – in other words, 400-900% more deadly than Ioannidis’s estimates.
It’s not about the question. It’s about the answers he gave without any evidence. He critized that actions were based on little data (which is true), but oppsed actions without data. Everyone was sure that IFR would be lower than the CFR known from other countries. But these were educated guesses. Decisions needed to be made based on the data available and models based on available data (which BTW already used a lower IFR)
It’s very difficult to blame others for political reactions when Ioannidis turned it political from the beginning. He came out with “Actions are wrong – because there is no evidence and I *guess* IFR is way lower”.
Yes, of course it’s important to determine the “true” IFR. But he should have asked the question without political statement which makes him look very biased. And yes, other groups also found low IFRs in some areas *but* 0.4 is still way higher than 0.1.
Andreas, I think you are making an unfair caricature of Ioannidis’ early statements. His point was that while much remained to be learned about the virus decisions were being made based on rather worst case assumptions incompatible with what evidence did exist. Moreover, his point was never a political one and the irrational political reactions were not excusable as you suggest. You are mistaken when you accuse him of making a political statement.
Well, the first reactions came from the conservative side praising him for “proofing that lockdown was bad”.
https://www.statnews.com/2020/03/17/a-fiasco-in-the-making-as-the-coronavirus-pandemic-takes-hold-we-are-making-decisions-without-reliable-data/
Do you remember this article?
>If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.”<
I do not get how even assume for a moment that it would stop at 1 %.
IIRC the models used IFR 0.9. That may be too high under *normal* circumstances without overwhelmed hospitals, but IFR was in that range in the worst hit regions.
And models estimated 100000-200000 *minimum* deaths. US is at 117840 and it will not end soon – esp. if mitigation is completely lifted.
Thanks for this he deserves it. I obviously don’t know him but he sure seems like a genuine and nice person
A thoughtful article. I also suspect that Ioannidis will be vindicated before this is over. (And for the record, I am not a political conservative, and I am a statistician.)
I have been reading the articles and papers written by Ioannidis throughout the pandemic, and summarize his two main points:
1.) There is a growing body of evidence that this coronavirus is not as dangerous as we thought in March. For instance, see the most recent estimates of the infection fatality rate from the CDC’s website (which this article mentions), or Ioannidis’ meta-analysis of seroprevalence studies, which this article also mentions.
2.) Lockdowns and some other aspects of the containment efforts cause tremendous harm to public health, not just to the economy. We should not ignore this.
With those two points in mind, we need to adjust our course since we now have more empirical data to inform our decisions.
I also note that British pathologist John Lee accused people of being “monomaniacal” during this pandemic by focusing almost exclusively on the death toll from COVID-19 and fears of collapsing the health care system. It is essential that we also consider the toll that drastic containment measures will have on public health. I find that avid supporters of lockdowns can be nonchalantly dismissive of the adverse effects that lockdowns have on public health–suicides, alcoholism, child abuse, cancer screenings missed, heart disease appointments missed, and other serious issues.
Just because there are partisan sides to a debate, doesn’t mean that both sides are wrong.
This article completely glosses over the methodological issues plaguing the Bendavid et al. preprint. It isn’t simply a matter that “all antibody tests have errors” — it’s that they didn’t control for those errors correctly in reporting their confidence intervals.
Let’s also not forget that the wife of one of the coauthors sent out a misleading, not IRB-approved email to recruit her friends to partake in the test, promising that antibodies grants immunities to COVID.
The article also doesn’t mention that many of the same authors did a subsequent study in LA, but released a press release before any preprint. Is that Undark’s endorsed approach toward science — science by press release? The preprint was only discovered because someone had leaked it to redstate.com — a right-wing media source. How did it get there?
These are all valid critiques of the sloppy science that Ionnadis has been doing lately that the article completely ignores.
The subsequent “tribalism” emerges because scientists — a mostly left-leaning group — generally rejected the paper on scientific grounds, whereas right-wing propagandists uncritically gobbled it up.
Sam, your opinions are expressed in the kind of derisive, petty and reductive sloganeering language that goes along with one-sided partisan politics. Your commentary has proven the primary point of the article. Which is that political partisanship has poisoned public opinions about science and this subjective bias has made it impossible for people to objectively assess the evidence purely on its own merits.
Yet I am the one who brings up scientific objections to the paper (and consequently this article), and you are making a carte blanche assertion that my objections constitute “derisive, petty and reductive sloganeering” without addressing any of the scientific content!
Lol funny how they rejected it on scientific grounds and he turned out to be completely right and vindicated. Funny how he could make all of those mistakes, which of course he has never been known for making in his professional career up to this point, and then just accidentally come up with the right answer. That people like you are still attacking him is just comedy at this point. If you are so scared, just stay home. I am going to do 2.6X more than I do to protect myself from the seasonal flu each year, which according to my calculations, is nothing.
Can you elaborate what you mean by “vindicated”? I don’t think we are looking at the same evidence here.
Nevermind that it doesn’t matter that Ionnadis’s previous record has no bearing on whether this science is good. If you are solely interested in previous records, perhaps I can remind you that Bendavid, the first author of the paper, already has one retraction on the books.
Funny how he wasn’t “vindicated” at all, and in fact his conclusions were completely wrong.
Sam,
Of course you are aware that Ioaniddis has expressed regret for the press release and has graciously acknowledged most of the shortcomings to which you refer. The adjustments made to compensate for those shortcomings did not disturb the general thrust of his conclusions. But of course you already know this.
Great article. As you point out, the questions he raises are consistent with his historical, fact-based approach. In these times, everything put out on Covid (or any subject, for that matter) immediately becomes politically charged, which is unfortunate and counterproductive. I suggest everyone read the book “Factfulness”, which deals with the very issues discussed here.
The problem isn’t with the questions that Dr. Ioannidis raised. The problem is that he gave incorrect answers to those questions. Answers unsupported by the data.
Perhaps the difficult questions he raises would be treated more fairly and with respect, if the research he himself is throwing into the fire wasn’t as blatantly flawed as they are.
Agree. And himself making political statments from the beginning, even before his own study.
Andreas, perhaps you can enlighten us as to what his political statements were?