I will re-post some thoughts I have previously shared from John P.A. Ioannidis who is a professor of medicine and thoughtful critic of medical research. He often raises good points about trends in research and research ethics. His view is that meta-analyses are mass produced, redundant, misleading, and conflicted [1]!
One criticism of meta-analyses in [1], using anti-depressants as a case study: "the results of several meta‐analytic evaluations that addressed the effectiveness of and/or tolerability for diverse antidepressants showed that their ranking of antidepressants was markedly different. These studies had been conducted by some of the best meta‐analysts in the world, all of them researchers with major contributions in the methods of meta‐analysis and extremely experienced in its conduct. However, among 12 considered drugs, paroxetine ranked anywhere from first to tenth best and sertraline ranked anywhere from second to tenth best."
I like this quote because it highlights the conflict of interest and misleading-ness(or at least reproducibility problems) with meta-analyses. Antidepressants have a huge amount of primary research dedicated to them. They also have the attention of researchers experienced in meta-analysis. Yet, meta-analyses do not agree with each other (and in fact they strongly disagree with each other).
> I will re-post some thoughts I have previously shared from John P.A. Ioannidis who is a professor of medicine and thoughtful critic of medical research
> In an editorial on STAT published March 17, 2020, Ioannidis called the global response to the COVID-19 pandemic a "once-in-a-century evidence fiasco" and wrote that lockdowns were likely an overreaction to unreliable data.[14] He estimated that the coronavirus could cause 10,000 U.S. deaths if it infected 1% of the U.S. population, and argued that more data was needed to determine if the virus would spread more.[28][5][14] The virus in fact eventually infected far more people, and would cause more than 600,000 deaths in the U.S.[29][28][5] Marc Lipsitch, Director of the Center for Communicable Disease Dynamics at the Harvard T.H. Chan School of Public Health, objected to Ioannidis's characterization of the global response in a reply that was published on STAT the next day after Ioannidis's.[30]
> Ioannidis widely promoted a study of which he had been co-author, "COVID-19 Antibody Seroprevalence in Santa Clara County, California", released as a preprint on April 17, 2020. It asserted that Santa Clara County's number of infections was between 50 and 85 times higher than the official count, putting the virus's fatality rate as low as 0.1% to 0.2%.[n 1][32][29] Ioannidis concluded from the study that the coronavirus is "not the apocalyptic problem we thought".[33] The message found favor with right-wing media outlets, but the paper drew criticism from a number of epidemiologists who said its testing was inaccurate and its methods were sloppy.
Okay then.
Nothing like spending a career picking apart people's research and then generating absolutely garbage research outside your field of expertise, that is widely criticized by people who are actually the experts in that field...as being inaccurate and sloppy.
COVID hit, dude went all Don Quixote seeing conspiracies everywhere, and then generated a paper that suited his personal biases...
Your comment is the worst kind of ad hominem. You're simply dismissing one of the most-cited scientists in the world because he wrote something you disagree with.
He was right about the IFR for Covid-19, by the way. Subsequent research has upheld the finding. The primary factor that influences average IFR is the age of the population you're looking at:
> All systematic evaluations of seroprevalence data converge that SARS-CoV-2 infection is widely spread globally. Acknowledging residual uncertainties, the available evidence suggests average global IFR of ~0.15% and ~1.5-2.0 billion infections by February 2021 with substantial differences in IFR and in infection spread across continents, countries and locations.
> The estimated age-specific IFR is very low for children and younger adults (e.g., 0.002% at age 10 and 0.01% at age 25) but increases progressively to 0.4% at age 55, 1.4% at age 65, 4.6% at age 75, and 15% at age 85. Moreover, our results indicate that about 90% of the variation in population IFR across geographical locations reflects differences in the age composition of the population and the extent to which relatively vulnerable age groups were exposed to the virus.
If you are worried about ad hominems then you should also be worried about Ioannidis, who has taken to Twitter to criticize grad students with orthodox conclusions about COVID for their physical appearance and lack of publications relative to full professors.
I was a fan of Ioannidis when he put out the estimates, but he has lost me since then. I'd rather go with the massive amount of researchers that have built their career studying these things, than the guy from a different field that feels he is better than everyone.
I mean, if your networks ops people are telling you the cause of a problem in your web application, and a manager comes in from another team and says he ran tcpdump and he thinks they are all wrong, and then the networks ops people show what he did wrong with tcpdump, but the manager sticks to his guns, who are you going to believe?
The man ran around screaming about how COVID wasn't a big deal and thus we needed to stop with the lockdowns and whatnot.
As our ERs, ICUs, and morgues were overwhelmed.
Which is still repeatedly happening, in regions with low vaccination and poor pandemic health regulations/orders. Except now it's lots of kids too young to get the vaccine or below age of being able to get the vaccine on their own without parental approval.
It would be exceedingly kind to say his "this isn't a big deal, not that many people percentage-wise are dying"...was at best incredibly heartless.
Ad hominem would be "he's a covid-denier, his research is worthless."
What I actually said was that he was an expert in research quality, and then promptly helped generate very shoddy research that was outside his area of expertise and ended up widely criticized by people actually in that field. Pointing that out is not ad hominem.
Extremely early-on in the pandemic he ran around shouting with no evidence that COVID was not very infectious and not deadly at all, and really, we needed to stop with these silly lockdowns and social distancing and masks and so on.
He then changed his tune to claim that actually covid was very infectious but this meant that the death rate was very low (and thus we needed to stop with these silly lockdowns and social distancing and masks and so on.)
Even his claim of very high infection / low mortality rate were true, it doesn't change the fact that ERs and ICUs have been repeatedly swamped, predominantly in countries, states and counties where political leaders are not employing standard pandemic control measures.
The man was literally shouting "THIS IS NO BIG DEAL STOP LIMITING OUR FREEDOM" while ERs and ICUs filled to the gills with dying people and in many places they literally could not burn the bodies fast enough.
His early claims were junk in part because COVID often was not mentioned or listed as cause of death, especially early in the pandemic.
PS:
> You're simply dismissing one of the most-cited scientists in the world
Appeal to authority. See? I can play that too. Maybe you can fool the average HNer into thinking citations imply credibility, but absolute junk research can end up highly cited because it was junk and many researchers sought to validate said research or pointed out its obvious flaws.
Next time, try arguing this stuff with someone who didn't work in a lab that was doing linguistic analysis of research, a project that existed because citation counts are completely worthless for identify novel research or evaluating its validity. And to head it off at the pass: yeeeeeees, even if you include journal impact factor.
Thanks for posting this. I did not know about about this darker side to Ioannidis.
I think lockdowns are/were a good thing. I won't necessarily fault Ioannidis for comments on lock downs on March 17, 2020 because that's pretty early on in. However, the April 17, 2020 pre-print story is pretty damning.
I will re-post some thoughts I have previously shared from John P.A. Ioannidis who is a professor of medicine and thoughtful critic of medical research. He often raises good points about trends in research and research ethics. His view is that meta-analyses are mass produced, redundant, misleading, and conflicted [1]!
One criticism of meta-analyses in [1], using anti-depressants as a case study: "the results of several meta‐analytic evaluations that addressed the effectiveness of and/or tolerability for diverse antidepressants showed that their ranking of antidepressants was markedly different. These studies had been conducted by some of the best meta‐analysts in the world, all of them researchers with major contributions in the methods of meta‐analysis and extremely experienced in its conduct. However, among 12 considered drugs, paroxetine ranked anywhere from first to tenth best and sertraline ranked anywhere from second to tenth best."
I like this quote because it highlights the conflict of interest and misleading-ness(or at least reproducibility problems) with meta-analyses. Antidepressants have a huge amount of primary research dedicated to them. They also have the attention of researchers experienced in meta-analysis. Yet, meta-analyses do not agree with each other (and in fact they strongly disagree with each other).
[1] https://pubmed.ncbi.nlm.nih.gov/27620683/