This has been a week chock-full of bias! First nature ran a cover story on it, with an editorial, and a very nice introduction into the subject by Regina Nuzzo. Then Malcolm Macleod and colleagues published a perspective in Plos Biology demonstrating limited reporting of measures to reduce the risk of bias in life sciences publications, and that there may be an inverse correlation between journal rank or prestige of the University from which the research originated and presence of measures to prevent bias. At the same time Jonathan Kimmelman’s group came out with a report in eLife in which they meta-analytically explored preclinical studies of an anticancer drug (sunitinib) to demonstrate that only a fraction of drugs that show promise in animals end up proving safe and effective in humans, partly because of design flaws, such as lack of prevention of bias, and partly due to positive publication bias. Both articles resulted in a worldwide media frenzy, including coverage by Nature and the lay press, here is an example from the Guardian. Retraction Watch interviewed Jonathan, while Malcolm spoke on BBC4.
Eine Sendung des Deutschlandfunk (ausgestrahlt 20.9.15) von Martin Hubert. Aus der Ankündigung: ‘Biomediziner sollen in ihren Laboren unter anderem nach Substanzen gegen Krebs oder Schlaganfall suchen. Sie experimentieren mit Zellkulturen und Versuchstieren, testen gewollte Wirkungen und ergründen ungewollte. Neuere Studien zeigen jedoch, dass sich bis zu 80 Prozent dieser präklinischen Studien nicht reproduzieren lassen.’ Hier der Link zum Audiostream bzw. zum Transkript.
(German only, sorry!)
Biomedicine currently suffers a ‚replication crisis‘: Numerous articles from academia and industry prove John Ioannidis’ prescient theoretical 2005 paper ‘Why most published research findings are false’ (Why most published research findings are false) to be true. On the positive side, however, the academic community appears to have taken up the challenge, and we are witnessing a surge in international collaborations to replicate key findings of biomedical and psychological research. Three important articles appeared over the last weeks which on the one hand further demonstrated that the replication crisis is real, but on the other hand suggested remedies for it:
Two consortia have pioneered the concept of preclinical randomized controlled trials, very much inspired by how clinical trials minimize bias (prespecification of a primary endpoint, randomization, blinding, etc.), and with much improved statistical power compared to single laboratory studies. One of them (Llovera et al.) replicated the effect of a neuroprotectant (CD49 antibody) in one, but not another model of stroke, while the study by Kleikers et al. failed to reproduce previous findings claiming that NOX2 inhibition is neuroprotective in experimental stroke. In Psychology, the Open Science Collaboration conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Disapointingly but not surprisingly, replication rates were low, and studies that replicated did so with much reduced effect sizes.
On March 17, 2015 five panelists from cognitive neuroscience and psychology (Sam Schwarzkopf, Chris Chambers, Sophie Scott, Dorothy Bishop, and Neuroskeptic) publicly debated “Is science broken? If so, how can we fix it?” . The event was organized by Experimental Psychology, UCL Division of Psychology and Language Sciences / Faculty of Brain Sciences in London.
The debate revolved around the ‘reproducibility crisis’, and covered false positive rates, replication, faulty statistics, lack of power, publication bias, study preregistration, data sharing, peer review, you name it. Understandably the event caused a stir in the press, journals, and the blogosphere (Nature, Biomed central, Aidan’s Aviary, The Psychologist, etc…).
Remarkably, some of the panelists (notably Sam Schwarzkopf) respectfully opposed the current ‘crusade for true science’ (to which I must confess I subscribe) by arguing that science is not broken at all, but rather, by trying to fix it we run the risk to wreck it for good. Already a few days before the official debate, he and Neuroskeptic had started to exchange arguments on Neuroskeptic’s blog. While both parties appear to agree that science can be improved, they completely disagree in their analysis of the current status of the scientific enterprise, and consequently also on action points.
This predebate argument directed my attention to a blog, which was run by Sam Schwarzkopf, or rather his alter ego, the ‘Devil’s neuroscientist’ for a short, but very productive period. Curiously, the Devil’s neuroscientist retired from blogging the night before the debate, threatening that there will be no future posts! This is sad, because albeit somewhat aggressively, but very much to the point, the Devil’s neuroscientist tried to debunk the thesis that there is any reproducibility crisis, that science is not self-correcting, that studies should be preregistered, etc. In other words, he was arguing against most of the issues raised and remedies suggested also on my pages. In passing, he provided a lot of interesting links to proponents on either side of the fence. Although I do not agree with many of his conclusions, his is by far the most thoughtful treatment on the subject. Most of the time I discuss with fellow scientist who dismiss problems of the current model of biomedical research I get rather unreflected comments. They usually simply celebrate the status quo as the best of all possible worlds and don’t get beyond the statement that there may be a few glitches, but that the model has evolved over centuries of undeniable progress. “If it’s not broken, don’t fix it.”
The Devil’s blog stimulated me to produce a short summary of key arguments of the current debate, to organize my own thoughts and as a courtesy to the busy reader. Continue reading
In 2009, Chalmers and Glasziou investigated sources of avoidable waste in biomedical research and estimated that its cumulative effect was that about 85% of research investment is wasted (Lancet 2009; 374: 86–89). Critical voices have since then questioned the exceedingly high number (85%), or claimed that because of non-linearity’s and idiosyncrasies of the biomedical research process a large number of failures are needed to produce a comparably small number of breakthroughs, and therefore hailed the remaining 15%. Waste is defined as ‘resources consumed by inefficient or non-essential activities’. Does progress really thrive on waste?
The MEDLINE currently indexes 5,642 journals. PubMed comprises more than 24 million citations for biomedical literature from MEDLINE. My field is stroke research. Close to 30.000 articles were published in 2014 on the topic ‘Stroke’ (clinical and experimental), more than 20.000 of them were peer reviewed original articles in the English language (Web of Science). That amounts to more than 50 articles every day. In 2014, 1700 of them were rodent studies, a mere 5 per day. Does (can) anyone read them? And should we read them? Do researchers worldwide every day produce knowledge worth publishing in 50 articles?
‘Scientific rigor and the art of motorcycle maintenance‘ was another recent fine analysis on reliability issues in current biomedicine (Munafo et al. Nat Biotechnol. 32:871-3). If you only want to read one article, this may be it. It nicely sums up the problems and suggests all the relevant measures (see most of my previous posts). But besides the reference to Robert Pirsig’s 1974 novel, what is new on the article is the comparison of the scientific enterprise to the automobile industry, which successfully responded to quality problems with structured quality control (for a more thorough treatment, see the previous post on trust and auditing). Here is their conclusion:
‘Science is conducted on the principle that it is self-correcting, but the extent to which this is true is an empirical question. The more that quality control becomes integrated into the scientific process itself, the more the whole process becomes one of continual improvement. Implementing this at the level of production implies a culture of incentivizing, educating and empowering those responsible for production, rather than policing quality after the fact with ‘quality inspectors’ (i.e., peer reviewers) or, even more distally, requiring attempts at replication. We think this insight, applied successfully to automobile manufacturing in the 1970s, can also be profitably applied to the practice of scientific research to build a more solid foundation of knowledge and accelerate the research endeavor.’
It is time to act!