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A New Record For Scientific Retractions?
sciencehabit writes "An investigating committee in Japan has concluded that a Japanese anesthesiologist, Yoshitaka Fujii, fabricated a whopping 172 papers over the past 19 years. Among other problems, the panel, set up by the Japanese Society of Anesthesiologists, could find no records of patients and no evidence medication was ever administered. 'It is as if someone sat at a desk and wrote a novel about a research idea,' the committee wrote in a 29 June summary report."
Peer review is not designed to catch fraud, although it can, as to work on the assumption that the work may be fraudulent would cost too much and would not even be effective against cleverer frauds. The only way to catch a clever fraud is to try and replicate their work, and this can only happen after publication, usually when another researcher tries to build on the original work. If you do this all fraud will eventually be caught, the best you can hope for in the long run, as a scientist committing fraud, is to be thought of as critically incompetent. For this reason fraud is rare among academic scientists, but is unfortunately more common among their commercial counterparts.
I disagree about the rarity, on the basis of empirical evidence, for example the recent paper in Science (IIRC, sorry I don't have the reference handy) in which a cancer researcher failed to replicate 46 out of 53 papers published -- all of them with peer review -- prior to embarking on new research in the field. Similar meta-studies have turned up astounding rates of non-reproducible results in other fields (some more than others -- sociology and IIRC social psychology topping the non-medical list).
One problem is that we have constructed a system that rewards the publication of positive results and punishes negative results published or unpublished. Punishes as in makes or breaks the entire career of young researchers, if the negative result occurs when they are up for tenure. Rewards as in ensures research funding and professional advancement as long as positive results keep flowing out.
Another fundamental problem that peer review has a terrible time with is confirmation bias. Science in general has a serious problem with confirmation bias. If one ever embarks on a study where one seeks evidence for some causal linkage associated with some phenomenon in a general population where the phenomenon occurs, one can always find exemplars that support your hypothesis. Lacking actual work to replicate your results using sound methodology (e.g. double blinded and/or conducted using competent statistical analysis, something still as rare as hen's teeth in science in general because to it is difficult to do statistics correctly in a complex problem, not easy, and certainly not easy as in covered in one or two undergrad stats courses which is all that it is probable that the researcher has ever taken) confirmation bias can not only worm its way into the literature, it can come to dominate entire fields as a significant fraction of scientists who do the reviewing for both publication and grants are "descended" from one or two original researchers and their papers. It can take decades for this to be discovered and work out in the wash.
Peer review works better in some disciplines than others. Math it works well, because there is literally nothing up a publisher's sleeve -- fraudulent publication is indeed impossible and even mistaken publication is relatively rare and conditional on involving math so difficult even the reviewers have a hard time following it. Physics and the very hard sciences are also fortunate in that it works decently (although less perfectly), at least where there is competition and the proper critical/skeptical eye applied to results new and old. At least there a mix of laboratory replication and strong requirements of consistency usually keep one out of the worst trouble.
A simple rule of thumb is: The more a result relies on population studies, especially ones conducted with any kind of selection process or worse selection process plus the actual modification of the data according to some heuristic or correction process, where the study itself is conducted from the beginning to confirm some given hypothesis, the more likely it is that the result (when published) is bullshit that will eventually, possibly decades later, turn out to be completely wrong. If you have enough places for a thumb to be subtly placed on the scales and the owner of the thumb has any sort of vested or open interest in the outcome, it is even odds or better that a teensy bit of pressure will be applied, quite possibly without even the intention of the researcher. Confirmation bias is not necessarily "fraud" -- it is just bad science, science poorly done.
There is a move afoot to do something about this. We know that it happens. We know why it happens. We know a number of things that we can do to reduce the probability of it happening -- for example requiring the open publication of all data and methods contemporary with any paper produced from them, permitting absolutely anybody to look at them and see if t
Even when the experts all agree, they may well be mistaken. --- Bertrand Russell.
I saw a study done recently where the author found that the results of many studies are quite difficult to reproduce, and he found that the more you tried to reproduce them and the more you talked publicly about your results, the more difficult it became to reproduce the results.
The problem is that researchers usually aren't approaching a study as "Lets do xxx and see what happens, then write about that". They've been funded by someone who has a particular result or proof point they'd like to see, or the study operator has a vested interest in the study outcome. At least an expectation of what they think they'll find.
Our happy little brains then lead us to that conclusion or desired outcome, and we'll gleefully ignore the things that detract from the results.
And yes, the guy who did this study of study results also found that his ability to reproduce his own results became more difficult as time went by.
For a couple of good examples of how this works, see the studies on salt and saturated fats in our diet. The intersalt study folks threw out 40% of the data that said that salt had no effect on health, suggesting that since its well known that salt affects your health that the people who weren't affected must be lying about their salt consumption. So almost half the data suggested no result, but it was discarded because it didn't fit with the desired determination. Same thing happened with saturated fats. The original researcher took 21 countries worth of data but only 5 of the 21 showed health issues that were allegedly correlated to the consumption of saturated fats. The other 16 showed no correlation at all. In fact, the real correlation was to high caloric, high sugar/carb, highly processed foods and health issues, not anything to do with saturated fats. There are cultures that eat 50-70% of their food intake as fat and they have little to no cancer, obesity or diabetes. Take one of those people and move them to the US or England and put them on our diet? They get fat and sick.
Of course, even when the study obviously sucks, the press can be counted on to come to conclusions that the study didn't even address.