Unsurprisingly, cognitive dissonance and confirmation bias all around, on all sides.
Science is only as objective and unbiased as the people implementing the methodology. Which is to say... well, everybody can figure that bit out.
Much more at the link.
Epidemiological studies can come up with some crazy results, causing some critics to wonder if they're really worthwhile. - LA Times:
"...Such studies make headlines every day, and often, as the public knows too well, they contradict each other. One week we may hear that pets are good for your health, the next week that they aren't. One month, cellphone use causes brain cancer; the next month, it doesn't.
..."What about the man on the street?" asks Stan Young, a statistician at the National Institute of Statistical Sciences in Research Triangle Park, N.C. "He reads about coffee causing and not causing cancer -- so many contradictory findings he begins to think, 'I don't trust anything these scientists are saying.' "
These critics say the reason this keeps happening is simple: Far too many of these epidemiological studies -- in which the habits and other factors of large populations of people are tracked, sometimes for years -- are wrong and should be ignored.
In fact, some of these critics say, more than half of all epidemiological studies are incorrect.
...[the criticisms] are "quite simplistic and exaggerated," says Dr. Meir Stampfer, a professor of epidemiology and nutrition at the Harvard School of Public Health and a professor of medicine at Harvard Medical School.
What's more, some things simply cannot be tested in randomized clinical trials.
...epidemiological studies have their minuses too, some of which are very well known. Suppose a study finds that coffee drinkers are more likely to get a certain disease. That doesn't mean coffee caused the disease. Other, perhaps unknown, factors (called "confounders" in the trade) that are unrelated to the coffee may cause it -- and if coffee drinkers are more likely to do this other thing, coffee may appear, incorrectly, to be the smoking gun.
...Despite their shortcomings, epidemiological studies are often taken seriously, so much so that they can change medical practice. Such was the case after dozens of epidemiological studies, including one large, frequently cited one that came out of Harvard in 1991, had shown that taking estrogen after menopause reduces the risk of women getting cardiovascular disease.
...Eventually, a randomized clinical trial was conducted, as part of the so-called Women's Health Initiative. Findings published in 2002 not only found no protection to the heart but actually reported some harm.
...In a provocative 2005 paper, Ioannidis examined the six most frequently cited epidemiological studies published from three major clinical journals between 1990 and 2003. He found that four of the six findings were later overturned by clinical trials.
...The studies that overturned each of these epidemiological findings, Ioannidis says, "caused major waves of surprise when they first appeared, because everybody had believed the observational studies. And then the randomized trials found something completely different."
...Why does this happen?
Young believes there's something fundamentally wrong with the method of observational studies -- something that goes way beyond that thorny little issue of confounding factors. It's about another habit of epidemiology some call data-mining.
Most epidemiological studies, according to Young, don't account for the fact that they often check many different things in one study. "They think it is fine to ask many questions of the same data set," Young says. And the more things you check, the more likely it becomes that you'll find something that's statistically significant -- just by chance, luck, nothing more...
Many epidemiologists do not agree with the critics' assertion that most epidemiological studies are wrong and that randomized studies are more reliable.
...Stampfer says, the two types of studies often test different things. "It's not an issue here that observational studies got it wrong and randomized trials got it right," he says, referring to the hormone replacement studies. "My view is that [both] were right and they were addressing different questions."
...Such arguments do not sway epidemiology's detractors.
Each time a study doesn't replicate, "they make a specific argument why the studies are different," Young says...
...The debate is unlikely to be resolved any time soon. "If you put five epidemiologists and five statisticians in a room and have this debate," Young says, "and try to get each one to convince the other side, at the end of the day it will still be five to five.""
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