Believe It Or Not, Most Published Research Findings Are Probably False

English: A Collection of Articles on Disease Mongering in PLoS Medicine Español: Portada del monográfico publicado en Public Library of Science - Medicine sobre promoción de enfermedades (Photo credit: Wikipedia)

The rise of the Internet has worked wonders for the public's access to science, but this has come with the side effect of a toxic combination of confirmation bias and Google, enabling us to easily find a study to support whatever it is that we already believe, without bothering to so much as look at research that might challenge our position — or the research that supports our position for that matter.

I'm certainly not immune myself from credulously accepting research that has later been called into question, even on this blog where I take great effort to take a skeptical approach and highlight false claims arising from research. Could it be the case that studies with incorrect findings are not just rare anomalies, but are actually representative of the majority of published research?

The claim that "most published research findings are false" is something you might reasonably expect to come out of the mouth of the most deluded kind of tin-foil-hat-wearing-conspiracy-theorist. Indeed, this is a statement oft-used by fans of pseudoscience who take the claim at face value, without applying the principles behind it to their own evidence. It is however, a concept that is actually increasingly well understood by scientists. It is the title of a paper written 10 years ago by the legendary Stanford epidemiologist John Ioannidis. The paper, which has become the most widely cited paper ever published in the journal PLoS Medicine, examined how issues currently ingrained in the scientific process combined with the way we currently interpret statistical significance, means that at present, most published findings are likely to be incorrect.

Richard Horton, the editor of The Lancet recently put it only slightly more mildly: "Much of the scientific literature, perhaps half, may simply be untrue." Horton agrees with Ioannidis' reasoning, blaming: "small sample sizes, tiny effects, invalid exploratory analyses, and flagrant conflicts of interest, together with an obsession for pursuing fashionable trends of dubious importance." Horton laments: "Science has taken a turn towards darkness."

Last year UCL pharmacologist and statistician David Colquhoun published a report in the Royal Society's Open Science in which he backed up Ioannidis' case: "If you use p=0.05 to suggest that you have made a discovery, you will be wrong at least 30 percent of the time." That's assuming "the most optimistic view possible" in which every experiment is perfectly designed, with perfectly random allocation, zero bias, no multiple comparisons and publication of all negative findings. Colquhorn concludes: "If, as is often the case, experiments are underpowered, you will be wrong most of the time."

The numbers above are theoretical, but are increasingly being backed up by hard evidence. The rate of findings that have later been found to be wrong or exaggerated has been found to be 30 percent for the top most widely cited randomized, controlled trials in the world's highest-quality medical journals. For non-randomized trials that number rises to an astonishing five out of six.

Over recent years Ioannidis' argument has received support from multiple fields. Three years ago, when drugs company Amgen tried to replicate the "landmark publications" in the field of cancer drug development for a report published in Nature, 47 out of 53 could not be replicated. When Bayer attempted a similar project on drug target studies, 65 percent of the studies could not be replicated.

The problem is being tackled head on in the field of psychology which was shaken by the Stapel affair in which one Dutch researcher fabricated data in over 50 fraudulent papers before being detected. The social sciences received another blow recently when Michael LaCour was accused of fabricating data; the case exposed how studies are routinely published without raw data ever being made available to reviewers.

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