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Health See other Health Articles Title: It Ain't Necessarily So: Why Much of the Medical Literature Is Wrong In 1897, eight-year-old Virginia O'Hanlon wrote to the New York Sun to ask, "Is there a Santa Claus?"[1] Virginia's father, Dr. Phillip O'Hanlon, suggested that course of action because "if you see it in the Sun, it's so." Today many clinicians and health professionals may share the same faith in the printed word and assume that if it says it in the New England Journal of Medicine (NEJM) or JAMA or The Lancet, then it's so. Putting the existence of Santa Claus aside, John Ioannidis[2] and others have argued that much of the medical literature is prone to bias and is, in fact, wrong. Given a statistical association between X and Y, most people make the assumption that X caused Y. However, we can easily come up with 5 other scenarios to explain the same situation. 1. Reverse Causality Given the association between X and Y, it is actually equally likely that Y caused X as it is that X caused Y. In most cases, it is obvious which variable is the cause and which is the effect. If a study showed a statistical association between smoking and coronary heart disease (CHD), it would be clear that smoking causes CHD and not that CHD makes people smoke. Because smoking preceded CHD, reverse causality in this case is impossible. But the situation is not always that clear-cut. Consider a study published in the NEJM that showed an association between diabetes and pancreatic cancer.[3] The casual reader might conclude that diabetes causes pancreatic cancer. However, further analysis showed that much of the diabetes was of recent onset. The pancreatic cancer preceded the diabetes, and the cancer subsequently destroyed the insulin-producing islet cells of the pancreas. Therefore, this was not a case of diabetes causing pancreatic cancer but of pancreatic cancer causing the diabetes. Mistaking what came first in the order of causation is a form of protopathic bias.[4] There are numerous examples in the literature. For example, an assumed association between breast feeding and stunted growth, [5] actually reflected the fact that sicker infants were preferentially breastfed for longer periods. Thus, stunted growth led to more breastfeeding, not the other way around. Similarly, an apparent association between oral estrogens and endometrial cancer was not quite what it seemed.[6] Oral estrogens may be prescribed for uterine bleeding, and the bleeding may be caused by an undiagnosed cancer. Therefore, when the cancer is ultimately diagnosed down the road, it will seem as if the estrogens came before the cancer, when in fact it was the cancer (and the bleeding) that led to the prescription of estrogens. Clearly, sometimes it is difficult to disentangle which factor is the cause and which is the effect. 2. The Play of Chance and the DICE Miracle Whenever a study finds an association between 2 variables, X and Y, there is always the possibility that the association was simply the result of random chance. Most people assess whether a finding is due to chance by checking if the P value is less than .05. There are many reasons why this the wrong way to approach the problem, and an excellent review by Steven Goodman[7] about the popular misconceptions surrounding the P value is a must-read for any consumer of medical literature. Post Comment Private Reply Ignore Thread
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