Friday, January 15, 2016


If we want medicine to be evidence-based, what should we think when the evidence doesn't agree?

Applies to all fields and activities.

A meta-analysis is nothing more than just a fancy weighted average of its component studies. We were surprised to find that approximately 63 percent of the included studies were unique to one or the other set of meta-analyses. In other words, despite the fact that the two sets of meta-analyses would presumably look for the same papers, using similar search criteria, over a similar period of time and from similar databases, only about a third of the papers the two sets had included were the same.
It seems likely that most or all of these differences come down to the fact that Cochrane insists on tougher criteria. A meta-analysis is only as good as the studies it includes, and taking the average of poor research can lead to a poor result. As the saying goes, "garbage in, garbage out."
Interestingly, the analyses that reported much higher effect sizes tended to get cited again in other papers at a much higher rate than the analyses reporting the lower effect size. This is a statistical embodiment of the old journalistic saying "If it bleeds, it leads." Big and bold effects get more attention than results showing marginal or equivocal outcomes. The  is, after all, just human.

Why does this matter?At its most basic level, this shows that Archie Cochrane was absolutely correct. Methodological consistency and rigor and transparency are essential. Without that, there's a risk of concluding that something works when it doesn't, or even just overhyping benefits.
But at a higher level this shows us, yet again, how very difficult it is to generate a unified interpretation of the medical literature. Meta-analyses are often used as the final word on a given subject, as the arbiters of ambiguity.
Clearly that role is challenged by the fact that two meta-analyses, ostensibly on the same topic, can reach different conclusions. If we view the meta-analysis as the "gold standard" in our current era of "evidence-based medicine," how is the average doctor or policymaker or even patient to react when two gold standards contradict each other? Caveat emptor. 

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