Why We Can’t Trust Most Medical Studies
John Timmer, Ars Technica’s science editor, reports on a recent survey of medical literature that found 95 percent of the results of observational studies on human health have failed replication when tested using a rigorous, double blind trial.
It’s possible to get the mental equivalent of whiplash from the latest medical findings, as risk factors are identified one year and exonerated the next. According to a panel at the American Association for the Advancement of Science, this isn’t a failure of medical research; it’s a failure of statistics, and one that is becoming more common in fields ranging from genomics to astronomy. The problem is that our statistical tools for evaluating the probability of error haven’t kept pace with our own successes, in the form of our ability to obtain massive data sets and perform multiple tests on them. Even given a low tolerance for error, the sheer number of tests performed ensures that some of them will produce erroneous results at random.
Why we can’t trust most medical studies
Statistical validation of results, as Shaffer described it, simply involves testing the null hypothesis: that the pattern you detect in your data occurs at random. If you can reject the null hypothesis—and science and medicine have settled on rejecting it when there’s only a five percent or less chance that it occurred at random—then you accept that your actual finding is significant.
The problem now is that we’re rapidly expanding our ability to do tests. Various speakers pointed to data sources as diverse as gene expression chips and the Sloan Digital Sky Survey, which provide tens of thousands of individual data points to analyze. At the same time, the growth of computing power has meant that we can ask many questions of these large data sets at once, and each one of these tests increases the prospects than an error will occur in a study; as Shaffer put it, “every decision increases your error prospects.” She pointed out that dividing data into subgroups, which can often identify susceptible subpopulations, is also a decision, and increases the chances of a spurious error. Smaller populations are also more prone to random associations.
In the end, Young noted, by the time you reach 61 tests, there’s a 95 percent chance that you’ll get a significant result at random. And, let’s face it—researchers want to see a significant result, so there’s a strong, unintentional bias towards trying different tests until something pops out.