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Don't Speak Out Against Conventional Wisdom - From the Wall Street Journal


John P.A. Ioannidis - Stanford C. F. REHNBORG PROFESSOR IN DISEASE PREVENTION IN THE SCHOOL OF MEDICINE, PROFESSOR OF MEDICINE, OF HEALTH RESEARCH AND POLICY (EPIDEMIOLOGY) AND BY COURTESY, OF STATISTICS AND OF BIOMEDICAL DATA SCIENCE Stanford Prevention Research Center Web page: http://web.stanford.edu/people/jioannid

Original article: https://www.wsj.com/articles/the-bearer-of-good-coronavirus-news-11587746176


Defenders of coronavirus lockdown mandates keep talking about science. “We are going to do the right thing, not judge by politics, not judge by protests, but by science,” California’s Gov. Gavin Newsom said this week. Michigan Gov. Gretchen Whitmer defended an order that, among other things, banned the sale of paint and vegetable seeds but not liquor or lottery tickets. “Each action has been informed by the best science and epidemiology counsel there is,” she wrote in an op-ed.

But scientists are almost never unanimous, and many appeals to “science” are transparently political or ideological. Consider the story of John Ioannidis, a professor at Stanford’s School of Medicine. His expertise is wide-ranging—he juggles appointments in statistics, biomedical data, prevention research and health research and policy. Google Scholar ranks him among the world’s 100 most-cited scientists. He has published more than 1,000 papers, many of them meta-analyses—reviews of other studies. Yet he’s now found himself pilloried because he dissents from the theories behind the lockdowns—because he’s looked at the data and found good news.

In a March article for Stat News, Dr. Ioannidis argued that Covid-19 is far less deadly than modelers were assuming. He considered the experience of the Diamond Princess cruise ship, which was quarantined Feb. 4 in Japan. Nine of 700 infected passengers and crew died. Based on the demographics of the ship’s population, Dr. Ioannidis estimated that the U.S. fatality rate could be as low as 0.025% to 0.625% and put the upper bound at 0.05% to 1%—comparable to that of seasonal flu.

“If that is the true rate,” he wrote, “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.”

Dr. Ioannidis, 54, likes metaphors. A New York native who grew up in Athens, he also teaches comparative literature and has published seven literary works—poetry and fiction, the latest being an epistolary novel—in Greek. In his spare time, he likes to fence, swim, hike and play basketball.

Early in his career, he realized that “the common denominator for everything that I was doing was that I was very interested in the methods—not necessarily the results but how exactly you do that, how exactly you try to avoid bias, how you avoid error.” When he began examining studies, he discovered that few headline-grabbing findings could be replicated, and many were later contradicted by new evidence.

Scientific studies are often infected by biases. “Several years ago, along with one of my colleagues, we had mapped 235 biases across science. And maybe the biggest cluster is biases that are trying to generate significant, spectacular, fascinating, extraordinary results,” he says. “Early results tend to be inflated. Claims for significance tend to be exaggerated.”

An example is a 2012 meta-analysis on nutritional research, in which he randomly selected 50 common cooking ingredients, such as sugar, flour and milk. Eighty percent of them had been studied for links to cancer, and 72% of the studies linked an ingredient to a higher or lower risk. Yet three-quarters of the findings were weak or statistically insignificant.

Dr. Ioannidis calls the coronavirus pandemic “the perfect storm of that quest for very urgent, spectacular, exciting, apocalyptic results. And as you see, apparently our early estimates seem to have been tremendously exaggerated in many fronts.”

Chief among them was a study by modelers at Imperial College London, which predicted more than 2.2 million coronavirus deaths in the U.S. absent “any control measures or spontaneous changes in individual behaviour.” The study was published March 16—the same day the Trump administration released its “15 Days to Slow the Spread” initiative, which included strict social-distancing guidelines.

Dr. Ioannidis says the Imperial projection now appears to be a gross overestimate. “They used inputs that were completely off in some of their calculation,” he says. “If data are limited or flawed, their errors are being propagated through the model. . . . So if you have a small error, and you exponentiate that error, the magnitude of the final error in the prediction or whatever can be astronomical.”

“I love models,” he adds. “I do a lot of mathematical modeling myself. But I think we need to recognize that they’re very, very low in terms of how much weight