The misuse of statistics in medical research

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Re: The misuse of statistics in medical research

Postby compared2what? » Tue Sep 25, 2012 11:08 pm

Oh, what the hell. In for a penny, in for a pound.

First of all, I see that Marcia Angell walked her rhetoric back somewhat in her response to the letters that predictably followed those articles, some of which (predictably) were just as specious as what they were critiquing, if not more so.

So. Credit where due: Her last line...

[i}t is no favor to desperate and vulnerable patients to treat them with drugs that have serious side effects unless it is clear that the benefits outweigh the harms.


...is so true that I don't really understand why it's so rarely framed that way. At least nine-tenths of my objections to most anti-psychiatry rhetoric would evaporate if it included some nod in the direction of there being a non-iatrogenic problem of some sort. Or really, ANY sort. (Not psychiatric, IOW.)

This, on the other hand...

Friedman and Nierenberg are right that the National Comorbidity Survey showed very little change in the prevalence of three particular types of mental disorders in adults between 1991 and 2003, although the increase in the percentage of people treated was dramatic. But the frequency of some diagnoses, such as bipolar disease and autism, has soared. Moreover, the survey showed a prevalence of mental illness of about 30 percent, which surely represents either a major epidemic or rampant overdiagnosis. One of the most remarkable findings was that 20 percent of randomly selected adults were undergoing treatment for emotional disorders at the time of the later survey, about half of whom did not even meet the DSM-IV criteria for a mental disorder.


...is just DISHONEST.

As far as the general stats go:

    * The prevalence rate for mental disorders in the United States has been about thirty percent for decades. It's not showing any signs of changing.

    * About twenty percent of those people (.06 percent of the total population)**** received treatment in 1991. And about thirty percent (.09 percent of the total population)**** did in 2003. While I guess that's arguably a dramatic increase, it's not exactly the most dramatic statistical truth in the picture.***

    * It's true that overall, twelve percent of the total population received some kind of treatment for a mental disorder from somebody, somewhere (ie -- from a GP, social services, a psychiatrist, other mental health professional, or alternative practitioner) in 1991, versus twenty percent in 2003. And that's dramatic. But half of that increase was made up of people who didn't have mental disorders. And the sector on the delivery side that had the biggest boom was "General Medical." So it's kind of equivocating to cite it as evidence that psychiatry WANTS YOU. (Although it might, for all I know.)

    * I don't know why Angell feels that thirty percent must represent rampant overdiagnosis or an epidemic. I mean, maybe it does. But since that figure includes all cases of alcoholism, substance abuse, PTSD, eating disorders, et cetera -- a lot of things that would maybe have a 3- to 5-ish prevalence rate individually (most of which are never treated, and many of which are transient) I don't see what's so incredibly outlandish about it. And she doesn't provide clue one. So it's a mystery.

    FWIW, prevalence for "serious mental illness" is about eight percent. That, to me, sounds high. But I don't know how they're defining it.

    * I already covered the twenty percent thing.

Shorter version: WRT the epidemic of mental illness, there is none. Most people who qualify for treatment don't receive any. Some of them might not need any. But some might. No way to say. That's a problem. But I don't know what kind. Misallocation of resources, I guess.

WRT overdiagnosis and overtreatment of mental illness among people who don't qualify for either, that's a real trend and a real problem. But it has virtually nothing to do with the desire of psychiatry to catch and keep you in its butterfly net. So you can't address it by raging at that. It won't help.

WRT raging at the many lapses of psychiatric treatment, whether pharmaceutical or otherwise: I'm all for it, in theory. Most of the .09 percent*** ofpeople who receive it for something resembling a reason get bad care, even when it's not abusive. And sometimes it is, of course. The system sucks.

_________________-

Full letters exchange on the Angell piece here. And one full overview-type write-up of the NCS survey results with tables and stuff is here. But that's a lot of data, there are reams of more specific studies based on it, too.
______________

***ON EDIT: That .06 and .09 just can't be right. Too low. I don't have time to check it now. But I think I must have gotten outplayed by a two-digit number. That happens sometimes.

****ON SECOND EDIT: It seems wrong whatever way I look at it. The figures that are still there are right (ie, according to the survey), though.
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Re: The misuse of statistics in medical research

Postby slimmouse » Fri Sep 28, 2012 5:08 pm

Im wondering if any of the defenders of the faith that is modern medicine, can quickly bring to our attention the prior evidence cited by the Big Boys, that proclaimed this drug safe and effective.

I doubt that they would attempt to even try, but of course that aint what they're here for. They're here to pick holes and fights. Otherwise, as opposed to spending their entire lives it seems, in a critical capacity of the sceptics of the modern medicine scam (amongst other stuff) they would probably be bringing our attention to things such as this :

A new study published in the journal Atherosclerosis found that statin use is associated with a 52% increased prevalence and extent of coronary artery plaques possessing calcium.[i] This study, published on August 24th, was preceded only three weeks earlier by one in the journal Diabetes Care, which found that coronary artery calcification "was significantly higher in more frequent statin users than in less frequent users," among patients with type 2 diabetes and advanced atherosclerosis.


http://www.greenmedinfo.com/blog/statin ... cification

A few billion bucks later, and the cost to benefits ratio is? Fuck modern medicine.

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Re: The misuse of statistics in medical research

Postby compared2what? » Sat Sep 29, 2012 5:03 am

Thanks.

I'd barely know anything about statins, except that one of them -- called Lipitor here, anyway-- which had been, like, the top-selling pharmaceutical of all time or something like it went off patent maybe a year or so ago, It was in the news, briefly. So. I kind of wonder whether the deal is now that statins are out and some new on-patent thing is in. A pharma plot. Basically. Is what I'm suggesting.

I always thought that was what happened with....Can't remember what it was called. The drug that used to be prescribed for stomach ulcers up until some point in the '90s, IIRC. It went off patent. And then suddenly there was a whole new theory of stomach ulcers that required some whole new treatment approach.

I didn't keep up with that one, though. So I don't know how it turned out.
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Re: The misuse of statistics in medical research

Postby Ben D » Sat Sep 29, 2012 6:06 am

Just browsing and note a bit of synchronicity here,...about a week ago my exwife had a bout of TGA (Transient Global Amnesia) which she was over after a night in hospital.

A couple of days later she emailed me telling me she had just recently completed a 30 day course of Lipitor prescribed by her GP for her high Cholesterol level, and to check out this link she had come across while searching for more information on TGA....http://www.spacedoc.com/lipitor_thief_of_memory.html

She certainly had good 'luck' in stumbling on that Astronaut's website, needless to say she will not allow herself to be persuaded to try it again.
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Re: The misuse of statistics in medical research

Postby Wombaticus Rex » Sat Sep 29, 2012 9:52 am

"The Cult of Statistical Significance" is an excellent and outright terrifying book.
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Re: The misuse of statistics in medical research

Postby compared2what? » Sat Sep 29, 2012 1:14 pm

Ben D wrote:Just browsing and note a bit of synchronicity here,...about a week ago my exwife had a bout of TGA (Transient Global Amnesia) which she was over after a night in hospital.

A couple of days later she emailed me telling me she had just recently completed a 30 day course of Lipitor prescribed by her GP for her high Cholesterol level, and to check out this link she had come across while searching for more information on TGA....http://www.spacedoc.com/lipitor_thief_of_memory.html

She certainly had good 'luck' in stumbling on that Astronaut's website, needless to say she will not allow herself to be persuaded to try it again.


Even if it was just for liability reasons, I'd imagine that nobody is going to try too hard to persuade her, given that she's already had an adverse reaction to it.

I have nothing for or against statins, personally. But fwiw, per this here length but informative .pdf, "The Use of Medicines in the United States" -- which makes it depressingly clear that practically the only people in the country who get regular health care are on Medicare -- there were 255 million prescriptions written for them in 2010. And Lipitor was the leading brand in the category.

Statins are the best-selling drugs in the world, and have been for a decade. Twenty million Americans are currently taking them, according to "The Latest Statin Scare: Are You At Risk?" (Forbes article, not exactly concealing the downsides, here.)

They don't sound like fun for everyone. And your friend's wife's experience sounds terrifying. One of my closest relatives went through something pretty similar about ten years ago, and that was pretty bad. But not as bad as the quintuple by-pass surgery that it led to, which ended up being a lot more than a one-night hospital stay, for one reason or another. That was bad enough that I guess I was repressing the memory, although (thankfully), it's all fine now and has been for years. So I'm glad you reminded me of it, causing me to realize: "Wait! I know someone who takes statins and is therefore still not only alive but healthy!"

I also know people who just had heart attacks and died, though. Happened to a guy I'd known/loved for almost all of my life (friend's father) a couple of months after the stuff in my family did. Same condition, pretty much. He wasn't a fan of modern medicine. And I don't really know what else to say about that. He was an incredible person. I really wouldn't have wished him any different. But I do wish he were still alive. He wasn't an old man. His wife of gazillon-and-one years is....Well. Blah, blah, blah. Basically. It was a bad deal.

There are an awful lot of people out there on various routes to that outcome that will probably include either statins or coronary heart disease, is I guess what I'm saying, Ben D. Way too many for any one person's experience to be definitive wrt one, or the other, or both. Or even for any one group of several thousand. The heart disease part of it is the longtime leading cause of death in the world, I think.

You and I can't possibly be the only people on the board who are acquainted with someone who's taken/is taking statins. Now that I come to think of it.
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Re: The misuse of statistics in medical research

Postby JackRiddler » Sat Sep 29, 2012 4:15 pm

compared2what? wrote:I always thought that was what happened with....Can't remember what it was called. The drug that used to be prescribed for stomach ulcers up until some point in the '90s, IIRC. It went off patent. And then suddenly there was a whole new theory of stomach ulcers that required some whole new treatment approach.


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Re: The misuse of statistics in medical research

Postby compared2what? » Sat Sep 29, 2012 8:05 pm

^^I'm not totally sure. But I think so. If Tagamet went off-patent shortly before ulcers became a bacterial thing, then yes.
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Re: The misuse of statistics in medical research

Postby JackRiddler » Sat Nov 10, 2012 11:01 pm

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Re: The misuse of statistics in medical research

Postby Sounder » Sun Nov 11, 2012 8:28 am

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182327/

Why Most Published Research Findings Are False
John P. A. Ioannidis




http://www.activistpost.com/2012/11/evi ... -flip.html


Sayer Ji, Contributor
Activist Post

....Case in point: in a 2005 essay, "Why Most Published Research Findings are False," and which is the most downloaded document of all time on PLoS, the Public Library of Medicine's peer-reviewed, open access journal, John P. A Ioannidis explains in detail how "It can be proven that most claimed research findings are false." And that "for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias."

The Atlantic published a piece on Ioannidis' work, back in 2010, titled "Lies, Damned Lies, and Medical Science," well worth reading, and which opened with "Much of what medical researchers conclude in their studies is misleading, exaggerated, or flat-out wrong. So why are doctors - to a striking extent - still drawing upon misinformation in their everyday practice?"....



The author puts blame at the feet of the FDA.

We all know that the FDA are not the only ones functioning as enforcers of normative conceptual structures.
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Re: The misuse of statistics in medical research

Postby JackRiddler » Tue Oct 29, 2013 3:24 pm

From the related thread

Notes on the Paradigm Crisis
rigorousintuition.ca/board2/viewtopic.php?f=33&t=34510

the following article copy-pasted by WR is fully on-topic here.

Thanks to the Wombat who posted it on Mon Oct 28, 2013 1:51 pm wrote:Nothing strictly new, some tedious moralizing, but an MSM overview of the problem and thus it is included here...

Via: http://www.latimes.com/business/la-fi-h ... z2j2lC4j00

Science has lost its way, at a big cost to humanity
By Michael Hiltzik

In today's world, brimful as it is with opinion and falsehoods masquerading as facts, you'd think the one place you can depend on for verifiable facts is science.

You'd be wrong. Many billions of dollars' worth of wrong.

A few years ago, scientists at the Thousand Oaks biotech firm Amgen set out to double-check the results of 53 landmark papers in their fields of cancer research and blood biology.

The idea was to make sure that research on which Amgen was spending millions of development dollars still held up. They figured that a few of the studies would fail the test — that the original results couldn't be reproduced because the findings were especially novel or described fresh therapeutic approaches.

But what they found was startling: Of the 53 landmark papers, only six could be proved valid.

"Even knowing the limitations of preclinical research," observed C. Glenn Begley, then Amgen's head of global cancer research, "this was a shocking result."

Unfortunately, it wasn't unique. A group at Bayer HealthCare in Germany similarly found that only 25% of published papers on which it was basing R&D projects could be validated, suggesting that projects in which the firm had sunk huge resources should be abandoned. Whole fields of research, including some in which patients were already participating in clinical trials, are based on science that hasn't been, and possibly can't be, validated.

"The thing that should scare people is that so many of these important published studies turn out to be wrong when they're investigated further," says Michael Eisen, a biologist at UC Berkeley and the Howard Hughes Medical Institute. The Economist recently estimated spending on biomedical R&D in industrialized countries at $59 billion a year. That's how much could be at risk from faulty fundamental research.

Eisen says the more important flaw in the publication model is that the drive to land a paper in a top journal — Nature and Science lead the list — encourages researchers to hype their results, especially in the life sciences. Peer review, in which a paper is checked out by eminent scientists before publication, isn't a safeguard. Eisen says the unpaid reviewers seldom have the time or inclination to examine a study enough to unearth errors or flaws.

"The journals want the papers that make the sexiest claims," he says. "And scientists believe that the way you succeed is having splashy papers in Science or Nature — it's not bad for them if a paper turns out to be wrong, if it's gotten a lot of attention."

Eisen is a pioneer in open-access scientific publishing, which aims to overturn the traditional model in which leading journals pay nothing for papers often based on publicly funded research, then charge enormous subscription fees to universities and researchers to read them.

But concern about what is emerging as a crisis in science extends beyond the open-access movement. It's reached the National Institutes of Health, which last week launched a project to remake its researchers' approach to publication. Its new PubMed Commons system allows qualified scientists to post ongoing comments about published papers. The goal is to wean scientists from the idea that a cursory, one-time peer review is enough to validate a research study, and substitute a process of continuing scrutiny, so that poor research can be identified quickly and good research can be picked out of the crowd and find a wider audience.

PubMed Commons is an effort to counteract the "perverse incentives" in scientific research and publishing, says David J. Lipman, director of NIH's National Center for Biotechnology Information, which is sponsoring the venture.

The Commons is currently in its pilot phase, during which only registered users among the cadre of researchers whose work appears in PubMed — NCBI's clearinghouse for citations from biomedical journals and online sources — can post comments and read them. Once the full system is launched, possibly within weeks, commenters still will have to be members of that select group, but the comments will be public.

Science and Nature both acknowledge that peer review is imperfect. Science's executive editor, Monica Bradford, told me by email that her journal, which is published by the American Assn. for the Advancement of Science, understands that for papers based on large volumes of statistical data — where cherry-picking or flawed interpretation can contribute to erroneous conclusions — "increased vigilance is required." Nature says that it now commissions expert statisticians to examine data in some papers.

But they both defend pre-publication peer review as an essential element in the scientific process — a "reasonable and fair" process, Bradford says.

Yet there's been some push-back by the prestige journals against the idea that they're encouraging flawed work — and that their business model amounts to profiteering. Earlier this month, Science published a piece by journalist John Bohannon about what happened when he sent a spoof paper with flaws that could have been noticed by a high school chemistry student to 304 open-access chemistry journals (those that charge researchers to publish their papers, but make them available for free). It was accepted by more than half of them.

One that didn't bite was PloS One, an online open-access journal sponsored by the Public Library of Science, which Eisen co-founded. In fact, PloS One was among the few journals that identified the fake paper's methodological and ethical flaws.

What was curious, however, was that although Bohannon asserted that his sting showed how the open-access movement was part of "an emerging Wild West in academic publishing," it was the traditionalist Science that published the most dubious recent academic paper of all.

This was a 2010 paper by then-NASA biochemist Felisa Wolfe-Simon and colleagues claiming that they had found bacteria growing in Mono Lake that were uniquely able to subsist on arsenic and even used arsenic to build the backbone of their DNA.

The publication in Science was accompanied by a breathless press release and press conference sponsored by NASA, which had an institutional interest in promoting the idea of alternative life forms. But almost immediately it was debunked by other scientists for spectacularly poor methodology and an invalid conclusion. Wolfe-Simon, who didn't respond to a request for comment last week, has defended her interpretation of her results as "viable." She hasn't withdrawn the paper, nor has Science, which has published numerous critiques of the work. Wolfe-Simon is now associated with the prestigious Lawrence Berkeley National Laboratory.

To Eisen, the Wolfe-Simon affair represents the "perfect storm of scientists obsessed with making a big splash and issuing press releases" — the natural outcome of a system in which there's no career gain in trying to replicate and validate previous work, as important as that process is for the advancement of science.

"A paper that actually shows a previous paper is true would never get published in an important journal," he says, "and it would be almost impossible to get that work funded."

However, the real threat to research and development doesn't come from one-time events like the arsenic study, but from the dissemination of findings that look plausible on the surface but don't stand up to scrutiny, as Begley and his Amgen colleagues found.

The demand for sexy results, combined with indifferent follow-up, means that billions of dollars in worldwide resources devoted to finding and developing remedies for the diseases that afflict us all is being thrown down a rathole. NIH and the rest of the scientific community are just now waking up to the realization that science has lost its way, and it may take years to get back on the right path.
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Re: The misuse of statistics in medical research

Postby Elihu » Tue Oct 29, 2013 3:35 pm

NIH and the rest of the scientific community are just now waking up to the realization that science has lost its way, and it may take years to get back on the right path.


take as many tax-funded years as you need. shareholders thank you for the un-earned income. some of the rest of us look forward to the first post-getting-your-&*%# - together contribution to society.
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Re: The misuse of statistics in medical research

Postby JackRiddler » Sat Mar 21, 2020 1:08 am

Seven years!!! Ack! But I remembered it.

First couple of posts are an Ioannidis primer. Seeing as he's come up again on the board.
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Re: The misuse of statistics in medical research

Postby Sounder » Sun Mar 22, 2020 3:50 pm

https://www.statnews.com/2020/03/17/a-f ... able-data/

A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data

By John P.A. Ioannidis

March 17, 2020
The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

Draconian countermeasures have been adopted in many countries. If the pandemic dissipates — either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable. How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?

Vaccines or affordable treatments take many months (or even years) to develop and test properly. Given such timelines, the consequences of long-term lockdowns are entirely unknown.
Related:
We know enough now to act decisively against Covid-19. Social distancing is a good place to start

The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.
Related:
Coronavirus model shows individual hospitals what to expect in the coming weeks

That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.

Could the Covid-19 case fatality rate be that low? No, some say, pointing to the high rate in elderly people. However, even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes. In fact, such “mild” coronaviruses infect tens of millions of people every year, and account for 3% to 11% of those hospitalized in the U.S. with lower respiratory infections each winter.

These “mild” coronaviruses may be implicated in several thousands of deaths every year worldwide, though the vast majority of them are not documented with precise testing. Instead, they are lost as noise among 60 million deaths from various causes every year.

Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases. In the U.S., for example, so far this season 1,073,976 specimens have been tested and 222,552 (20.7%) have tested positive for influenza. In the same period, the estimated number of influenza-like illnesses is between 36,000,000 and 51,000,000, with an estimated 22,000 to 55,000 flu deaths.

Note the uncertainty about influenza-like illness deaths: a 2.5-fold range, corresponding to tens of thousands of deaths. Every year, some of these deaths are due to influenza and some to other viruses, like common-cold coronaviruses.

In an autopsy series that tested for respiratory viruses in specimens from 57 elderly persons who died during the 2016 to 2017 influenza season, influenza viruses were detected in 18% of the specimens, while any kind of respiratory virus was found in 47%. In some people who die from viral respiratory pathogens, more than one virus is found upon autopsy and bacteria are often superimposed. A positive test for coronavirus does not mean necessarily that this virus is always primarily responsible for a patient’s demise.


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.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.

Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have.

In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates. But they may also backfire if children socialize anyhow, if school closure leads children to spend more time with susceptible elderly family members, if children at home disrupt their parents ability to work, and more. School closures may also diminish the chances of developing herd immunity in an age group that is spared serious disease.

This has been the perspective behind the different stance of the United Kingdom keeping schools open, at least until as I write this. In the absence of data on the real course of the epidemic, we don’t know whether this perspective was brilliant or catastrophic.

Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory. A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.


Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity.

One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.

In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.

The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died.

One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.

If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.


John P.A. Ioannidis is professor of medicine, of epidemiology and population health, of biomedical data science, and of statistics at Stanford University and co-director of Stanford’s Meta-Research Innovation Center.
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Re: The misuse of statistics in medical research

Postby MacCruiskeen » Mon Mar 30, 2020 8:13 am

Good on John Ioannidis, once again. He's a brave man..

See OffGuardian and @offguardian0 for many other rational & courageous scientists & medics trying hard to make their voices heard above this media-amplified fearporn pseudoscience disinfo cacophony.
"Ich kann gar nicht so viel fressen, wie ich kotzen möchte." - Max Liebermann,, Berlin, 1933

"Science is the belief in the ignorance of experts." - Richard Feynman, NYC, 1966

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