QUOTE (ww4321 @ Jul 15 2011, 06:16 AM)
So if these screenings and procedures don't prolong our lives, what does?Why Almost Everything You Hear About Medicine Is Wrong
Where is the documentation?
Statins -Cholestrol medicine has way cut way down deaths from heart disease in the last 20 years
“People are being hurt and even dying” because of false medical claims, he says: not quackery, but errors in medical research.
It’s a disturbing view, with huge implications for doctors, policymakers, and health-conscious consumers. And one of its foremost advocates, Dr. John P.A. Ioannidis, has just ascended to a new, prominent platform after years of crusading against the baseless health and medical claims.
A major study concluded there’s no good evidence that statins (drugs like Lipitor and Crestor) help people with no history of heart disease
. The study, by the Cochrane Collaboration, a global consortium of biomedical experts, was based on an evaluation of 14 individual trials with 34,272 patients. Cost of statins: more than $20 billion per year, of which half may be unnecessary.
Even a cursory glance at medical journals shows that once heralded studies keep falling by the wayside. Two 1993 studies concluded that vitamin E prevents cardiovascular disease; that claim was overturned by more rigorous experiments, in 1996 and 2000. A 1996 study concluding that estrogen therapy reduces older women’s risk of Alzheimer’s was overturned in 2004. Numerous studies concluding that popular antidepressants work by altering brain chemistry have now been contradicted (the drugs help with mild and moderate depression, when they work at all, through a placebo effect), as has research claiming that early cancer detection (through, say, PSA tests) invariably saves lives.
The list goes on.
Surgical practices, for instance, have not been tested to nearly the extent that medications have. “I wouldn’t be surprised if a large proportion of surgical practice is based on thin air, and [claims for effectiveness] would evaporate if we studied them closely,”
Ioannidis says.Medicine NewsweekLies, Damned Lies, and Medical Science
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? Dr. John Ioannidis has spent his career challenging his peers by exposing their bad science.
That question has been central to Ioannidis’s career. He’s what’s known as a meta-researcher, and he’s become one of the world’s foremost experts on the credibility of medical research. He and his team have shown, again and again, and in many different ways, that much of what biomedical researchers conclude in published studies—conclusions that doctors keep in mind when they prescribe antibiotics or blood-pressure medication, or when they advise us to consume more fiber or less meat, or when they recommend surgery for heart disease or back pain—is misleading, exaggerated, and often flat-out wrong. He charges that as much as 90 percent of the published medical information that doctors rely on is flawed. His work has been widely accepted by the medical community; it has been published in the field’s top journals, where it is heavily cited; and he is a big draw at conferences. Given this exposure, and the fact that his work broadly targets everyone else’s work in medicine, as well as everything that physicians do and all the health advice we get, Ioannidis may be one of the most influential scientists alive. Yet for all his influence, he worries that the field of medical research is so pervasively flawed, and so riddled with conflicts of interest, that it might be chronically resistant to change—or even to publicly admitting that there’s a problem.
It didn’t turn out that way. In poring over medical journals, he was struck by how many findings of all types were refuted by later findings. Of course, medical-science “never minds” are hardly secret. And they sometimes make headlines, as when in recent years large studies or growing consensuses of researchers concluded that mammograms, colonoscopies, and PSA tests are far less useful cancer-detection tools than we had been told; or when widely prescribed antidepressants such as Prozac, Zoloft, and Paxil were revealed to be no more effective than a placebo for most cases of depression; or when we learned that staying out of the sun entirely can actually increase cancer risks; or when we were told that the advice to drink lots of water during intense exercise was potentially fatal; or when, last April, we were informed that taking fish oil, exercising, and doing puzzles doesn’t really help fend off Alzheimer’s disease, as long claimed. Peer-reviewed studies have come to opposite conclusions on whether using cell phones can cause brain cancer, whether sleeping more than eight hours a night is healthful or dangerous, whether taking aspirin every day is more likely to save your life or cut it short, and whether routine angioplasty works better than pills to unclog heart arteries.
His model predicted, in different fields of medical research, rates of wrongness roughly corresponding to the observed rates at which findings were later convincingly refuted: 80 percent of non-randomized studies (by far the most common type) turn out to be wrong, as do 25 percent of supposedly gold-standard randomized trials, and as much as 10 percent of the platinum-standard large randomized trials. The article spelled out his belief that researchers were frequently manipulating data analyses, chasing career-advancing findings rather than good science, and even using the peer-review process—in which journals ask researchers to help decide which studies to publish—to suppress opposing views
Indeed, nutritional studies aren’t the worst. Drug studies have the added corruptive force of financial conflict of interest.
Medical research is not especially plagued with wrongness. Other meta-research experts have confirmed that similar issues distort research in all fields of science, from physics to economics (where the highly regarded economists J. Bradford DeLong and Kevin Lang once showed how a remarkably consistent paucity of strong evidence in published economics studies made it unlikely that any of them were right).http://www.theatlantic.com/magazine/archiv...l-science/8269/Publication Bias
... the tendency of scientists and scientific journals to prefer positive data over null results, which is what happens when no effect is found. The bias was first identified by the statistician Theodore Sterling, in 1959, after he noticed that ninety-seven per cent of all published psychological studies with statistically significant data found the effect they were looking for.
Sterling saw that if ninety-seven per cent of psychology studies were proving their hypotheses, either psychologists were extraordinarily lucky or they published only the outcomes of successful experiments. In recent years, publication bias
has mostly been seen as a problem for clinical trials, since pharmaceutical companies are less interested in publishing results that aren’t favorable. But it’s becoming increasingly clear that publication bias
also produces major distortions in fields without large corporate incentives, such as psychology and ecology.
... an equally significant issue is the selective reporting
of results—the data that scientists choose to document in the first place. Palmer’s most convincing evidence relies on a statistical tool known as a funnel graph. When a large number of studies have been done on a single subject, the data should follow a pattern: studies with a large sample size should all cluster around a common value—the true result—whereas those with a smaller sample size should exhibit a random scattering, since they’re subject to greater sampling error. This pattern gives the graph its name, since the distribution resembles a funnel.
The funnel graph visually captures the distortions of selective reporting
. For instance, after Palmer plotted every study of fluctuating asymmetry, he noticed that the distribution of results with smaller sample sizes wasn’t random at all but instead skewed heavily toward positive results. Palmer has since documented a similar problem in several other contested subject areas. “Once I realized that selective reporting
is everywhere in science, I got quite depressed,” Palmer told me.
One of the classic examples of selective reporting concerns the testing of acupuncture in different countries. While acupuncture is widely accepted as a medical treatment in various Asian countries, its use is much more contested in the West. These cultural differences have profoundly influenced the results of clinical trials. Between 1966 and 1995, there were forty-seven studies of acupuncture in China, Taiwan, and Japan, and every single trial concluded that acupuncture was an effective treatment. During the same period, there were ninety-four clinical trials of acupuncture in the United States, Sweden, and the U.K., and only fifty-six per cent of these studies found any therapeutic benefits. As Palmer notes, this wide discrepancy suggests that scientists find ways to confirm their preferred hypothesis, disregarding what they don’t want to see
The problem of selective reporting is rooted in a fundamental cognitive flaw, which is that we like proving ourselves right and hate being wrong
. “It feels good to validate a hypothesis,” Ioannidis said. “It feels even better when you’ve got a financial interest in the idea or your career depends upon it. And that’s why, even after a claim has been systematically disproven”—he cites, for instance, the early work on hormone replacement therapy, or claims involving various vitamins—“you still see some stubborn researchers citing the first few studies that show a strong effect.Bias science